# Sattyam Jain (SattyamJJain) - Complete Portfolio Context

> **Creator of pyAGI (Acquired by AGI House)** | GenAI Architect | Tech Lead
>
> Website: [sattyamjjain.in](https://sattyamjjain.in) | Updated: April 26, 2026

> Model posture (as of 2026-04-26): production workloads primarily on Claude Opus 4.7
> + Claude Sonnet 4.6, Claude Haiku 4.5, and GPT-5.5 / GPT-5.5 Pro (released
> 2026-04-23/24), with Gemini 3.1 Pro for evals. Anthropic's 2026-04-23 engineering
> postmortem fixed three Mar–Apr response-quality degradations; Claude Opus 4.7
> Auto Memory is available opt-in and default-off since 2026-04-23.
>
> Anthropic's Mythos Preview (Apr 7, limited release under Project Glasswing) is
> excluded from production posture by design — it is gated to security-research
> partners. Production agent-defense work continues on Opus 4.7 / Sonnet 4.6 /
> Haiku 4.5 + GPT-5.5 / GPT-5.5 Pro + Gemini 3.1 Pro for evals.

---

## Quick Reference

| Field | Value |
|-------|-------|
| **Name** | Sattyam Jain (SattyamJJain) |
| **Current Role** | Tech Lead / GenAI Architect at Attri.ai |
| **Specialization** | GenAI, Multi-Agent Systems, Production AI Platforms |
| **Key Achievement** | Creator of pyAGI (Acquired by AGI House) |
| **Location** | Ahmedabad, Gujarat, India |
| **Email** | sattyamjain96@gmail.com |
| **Book a Call** | [cal.com/sattyamjjain](https://cal.com/sattyamjjain) |
| **LinkedIn** | [linkedin.com/in/sattyamjain](https://linkedin.com/in/sattyamjain) |
| **GitHub** | [github.com/sattyamjjain](https://github.com/sattyamjjain) |
| **Twitter** | [@Sattyamjjain](https://twitter.com/Sattyamjjain) |
| **1-on-1 Session** | [topmate.io/sattyam_jjain](https://topmate.io/sattyam_jjain) |
| **Availability** | Open for consulting, advisory, and Tech Lead roles |

### Key Highlights (The "Tech Lead Signals")

- **pyAGI ACQUIRED** by Kyle Morris (co-founder of banana.dev serving 1,000+ startups, AGI House founding member) and Jeffrey
- **99.9% platform uptime** on Agentify multi-agent platform at Attri.ai
- **70% cost reduction** achieved through agentic automation (manual ops → autonomous)
- **15+ production AI agents** built (Research, Billing, Triage, Code Review, etc.)
- **MannSetu**: AI mental wellness platform serving 50+ active users in India
- **Deep Learning Specialization** by Andrew Ng (Coursera)
- **50+ original GitHub repositories** with 219+ stars

---

## About

I am Sattyam Jain, a Tech Lead specializing in Generative AI, multi-agent systems, and cloud-native architectures. Over the past 6+ years, I have designed and shipped production-grade AI platforms across fintech, AI training, and GenAI startups, consistently focusing on real business impact rather than just demos. At Attri.ai, I lead the development of agentic AI systems that help teams build products in minutes using AI agents, combining my experience in backend engineering, LLM orchestration, and scalable cloud infrastructure.  

My work spans everything from multi-agent orchestration and autonomous code generation to mental wellness AI (MannSetu), financial inclusion (MyShubhLife), and AI training platforms (Zenarate). I love building systems that not only work in production but also have measurable outcomes — including 70% cost reduction, 99.9% uptime, and thousands of end-users actively relying on my platforms.

In my current role as Tech Lead (AI Architecture) at Attri.ai, I am responsible for leading the design and implementation of Agentify, a multi-agent platform that powers real product development workflows. I work closely with founders, product managers, and designers to translate business requirements into robust technical architectures, then guide the engineering team through implementation, testing, and observability. My focus is on building an AI-first experience where agents like Orchestrator, PRD, Architect, Designer, Coder, and Discovery collaborate to deliver real outcomes for users.

Day to day, I design multi-agent flows, integrate LLMs like GPT-5.5, Claude Opus 4.7, and Claude Sonnet 4.6 reasoning models, manage MCP integrations, set up micro-VM infrastructure (E2B) for secure code execution, and ensure the entire system is observable via Datadog, Sentry, and PostHog. I also take ownership of reliability and cost-efficiency, shaping everything from token-usage strategies to multi-tenant architecture and zero-downtime deployment pipelines.

I am driven by the idea of using AI to solve real problems for real people. I am not excited by just building fancy demos — I am excited when a product I built helps a student manage exam anxiety, a support agent learn faster, or a startup team ship a production feature in hours instead of weeks. That is why I spend so much time on platforms like Agentify and MannSetu, where AI directly impacts productivity and mental wellness.

I also have a strong passion for open source and developer tooling. Projects like pyAGI and pyPantry exist because I wanted better tools for myself and for the community. When pyAGI was acquired and integrated into the broader AGI House ecosystem, it reinforced my belief that well-crafted open-source work can have outsized impact beyond its initial scope.

Architecturally, I lean towards modular, event-driven, and observable systems. For AI-heavy products, I like to separate concerns between orchestration, inference, storage, and presentation. I use microservices and serverless patterns where they make sense, and I choose between managed services and custom deployments based on cost, performance, and control requirements.

I also design systems with failure in mind—LLM API timeouts, network glitches, degraded dependencies, and unexpected user behavior. That is why I build fallback strategies, circuit breakers, retry policies, and queues into my architectures. Whether it is a multi-agent GenAI platform or a mental wellness app, my goal is to make the system resilient and easy to operate in production.

When I approach a problem, I start from the outcome and work backward. I like to define what a successful solution looks like in measurable terms—uptime, latency, cost per request, conversion rate, or user satisfaction—and then design the system to hit those targets. I break down complex problems into smaller, verifiable steps, and I make sure each step can be observed, tested, and rolled back if needed.

I am comfortable working under ambiguity and iterating quickly, but I avoid cutting corners on fundamentals like data integrity, security, and maintainability. I also prefer to validate ideas with prototypes, logs, and user feedback rather than assumptions, and I am not afraid to refactor or redesign when the data shows that our initial approach is not working.

## Experience

At Attri.ai, I work as Tech Lead (AI Architecture), leading GenAI and multi-agent systems development. I joined in late 2024 and quickly took ownership of architecting Agentify, Attri’s flagship multi-agent platform that helps teams build products in minutes using AI agents. I collaborate directly with founders on product direction, architecture decisions, and roadmap planning, while also guiding the engineering team through execution.  

My work spans backend services, agent orchestration, LLM integrations, observability, billing, and infrastructure. I am responsible for ensuring the platform achieves enterprise-level reliability (99.9% uptime), strong cost efficiency, and an agentic workflow that users actually trust in production.

At Zenarate, I worked as Software Development Engineer III from December 2023 to November 2024 in Gurugram, Haryana (Hybrid). I leveraged GPT models with prompt engineering for private and open-source LLMs, improving response times by 40%. I boosted predictive reporting accuracy by 15% through AI/ML solutions via Azure CLU and LUIS, enhancing client decision-making speed by 10%.

I redesigned system architecture, reducing downtime by 30% and improving AI deployment speeds by 25%. I built and optimized RESTful APIs using Flask/FastAPI, ensuring secure and scalable backend systems. I deployed scalable systems using AWS, managing infrastructure as code, and ensuring high availability while collaborating with cross-functional teams.

At MyShubhLife, I worked in Bengaluru, Karnataka from December 2020 to November 2023, progressing from Software Developer to Software Engineering Team Lead. As Team Lead (May 2023 - November 2023), I played a key role in developing Kautilya LMS, collaborating with a highly skilled team to deliver a robust SaaS product. I reduced partner onboarding time by 50% by implementing a streamlined, less tech-dependent onboarding process.

As Software Developer (December 2020 - May 2023), I built a tool for ERP control named Heimdall from scratch for real-time visibility into partner integrations. I worked as a full-time SDE on Kautilya LMS SaaS project serving 500+ users with course management and certification. I also developed backend services and APIs for loan processing, customer onboarding, and financial analytics.

At IDRONECT - The Drone Management Platform, I worked as a Full Stack Developer (Freelance) from September to November 2020, based remotely in Ghent, Belgium. I developed a real-time drone processing web app using ReactJS and NodeJS, significantly improving performance and user satisfaction.

I designed and implemented a high-performance Drone Simulator using CesiumJS, enhancing mapping precision and visualization. I improved advanced map features within the simulator, focused on optimizing app performance and scalability to handle real-time drone data efficiently, and fostered successful global partnerships by delivering robust solutions tailored to international client needs.

At Actonate, I worked as a Software Developer Intern from January to June 2020 in Vadodara, Gujarat. I played a pivotal role in a live project, utilizing ReactJS, NodeJS, Postman, and Java to overcome design and deployment challenges.

I collaborated with team leads to translate complex use cases into functional features, ensuring seamless user experiences. I contributed significantly across the product development lifecycle from initial design to deployment, tackled and resolved critical issues optimizing performance and reliability, and leveraged expertise in streaming protocols to enhance real-time functionality within the project.

## Projects

pyAGI is one of my flagship open-source projects: an autonomous agent framework for Python development using OpenAI models. It is designed to take a high-level objective and generate application descriptions, architecture outlines, UX flows, and even code steps using a sequence of LLM-powered prompts and LangChain-based chains.  

The project gained significant traction and was eventually acquired by Kyle Morris (co-founder of banana.dev and AGI House founding member) and Jeffrey, becoming part of the AGI House portfolio. This acquisition validated my approach to autonomous agents and opened up further opportunities to work at the intersection of open source and cutting-edge AI.

Agentify is a multi-agent platform I lead at Attri.ai with the tagline 'Build Products in Minutes with AI Agents.' It provides 15+ core specialized agents (Orchestrator, PRD, Architect, Designer, Coder, Diff, Discovery) plus a library of 100+ pre-built agent templates across domains like Finance, Contract Management, Customer Relations, Compliance, and Content Marketing.

The platform combines GenAI, real-time collaboration, MCP-style tool integrations, multi-tenant SaaS design, and robust observability. It's a full-stack agentic environment that plugs into existing developer workflows, tools, and repositories.

MannSetu is an AI-powered mental wellness platform I am building for Indian users. It offers voice-first conversations through Mithra, an AI wellness companion that supports Hindi, English, and Hinglish, with real-time voice emotion analysis and culturally-aware guidance. The platform is DPDP Act 2023 compliant and hosts data on Indian servers, reflecting a strong focus on privacy and local regulations.  

MannSetu targets students, working professionals, and young adults dealing with stress, anxiety, exam pressure, relationship issues, and workplace mental health challenges. The goal is to bridge the mental health treatment gap in India with accessible, 24/7 AI support.

I solo-built and launched whycantwehaveanagentforthis.com — a consumer AI tool that analyzes everyday problems and generates AI agent feasibility analyses. It uses Claude API for intelligent analysis with verdict tiers (Build It Yesterday, Strong Case, Worth Exploring, Not Yet, Don't Bother), real competitor research, and kill predictions. Built with Next.js, TypeScript, Vercel, and Upstash Redis. Launched during Holi 2026 with zero marketing spend. Achieved 157+ visitors from 5+ countries in Week 1, with Hacker News as the #1 traffic source. Operating cost is only $3-5/day.

I built Mnemo — an MCP-native embedded memory database for AI agents, written in Rust. Mnemo's primitives are REMEMBER, RECALL, FORGET, and SHARE, exposed as MCP tools that any AI agent can connect to directly. Key features include hybrid retrieval (Reciprocal Rank Fusion combining semantic vectors, BM25 keywords, knowledge graph signals, and recency scoring), AES-256-GCM encryption at rest, SHA-256 hash chain integrity, memory poisoning detection, cognitive forgetting (5 strategies), branching and replay of memory timelines, RBAC with scoped delegation, and ONNX local embeddings. Supports 4 access protocols (MCP stdio, REST, gRPC, pgwire) and has SDKs for Python, TypeScript, and Go. 15 framework integrations including OpenAI Agents SDK, LangGraph, CrewAI, Google ADK, Pydantic AI, AutoGen, and more. 132 tests. DuckDB and PostgreSQL backends supported.

I built Agent-Airlock — the open-source security firewall for AI agents, published on PyPI. It provides one-decorator security with ghost argument stripping (blocks LLM-hallucinated parameters), strict type validation, self-healing LLM-friendly errors, E2B sandbox execution, RBAC, PII/secret masking, network airgap, path validation, circuit breaker, OpenTelemetry observability, and cost tracking. Works with 9 frameworks: LangChain, LangGraph, OpenAI Agents SDK, PydanticAI, CrewAI, LlamaIndex, AutoGen, smolagents, and Anthropic. 1,157 tests with 79%+ coverage. 7 releases on PyPI. Addresses OWASP Top 10 for LLMs (2025). MIT licensed.

I built Verdict — the first universal quality evaluation plugin for Claude Code and Cowork. It auto-evaluates skill and agent execution quality with 7-dimension scoring (correctness, completeness, adherence, actionability, efficiency, safety, consistency), configurable rubrics, and dual-mode operation (automatic via hooks or on-demand via /judge command). Features persistent scorecards, blocking on critical failures, and benchmark suites. Ships with sensible defaults that can be overridden per-skill or per-team. MIT licensed. Published as v1.0.0 release on GitHub.

PyVerseAI is a collection of 14+ AI and cloud-based projects that I created to explore real-world problem-solving across domains. The collection includes IoT sensor simulations using MQTT, web scrapers for marketplaces like Amazon and Flipkart, real-time telemetry systems, thermal sensor visualization tools, and a Verilog hierarchy analyzer.  

PyVerseAI showcases my ability to combine AI, cloud, and systems thinking across diverse use cases, demonstrating full-stack capabilities from data ingestion to visualization.

AgentX is a project where I explore building a unified AI agent framework that integrates tools like LangChain, Whisper, Qwen2-VL, Mem0, Playwright, and OpenLLMetry. The aim is to create a flexible agent that can handle research, automation, and real-time decision-making across multiple modalities—text, audio, and web.

AgentX is also a playground for experimenting with evaluation, logging, and monitoring of complex agent workflows.

AIWTF is a playground repository I created to experiment with multiple generative AI tools, RAG setups, and open-source agent frameworks. It includes projects built with LangGraph, SmolAgents, AutoGen, and other libraries, allowing me to compare patterns and learn what works best in practice.

This playground enables rapid prototyping and cross-comparison of approaches before I bring the best patterns into production platforms like Agentify or MannSetu.

Dynamic-ML-Orchestration is a project focused on building a dynamic pipeline for ML workflows, especially around sentiment analysis and real-time decision-making. It uses AWS Lambda, S3, DynamoDB, SageMaker, and orchestration logic to handle ingestion, processing, and retrieval in a serverless architecture.

The goal of this project is to demonstrate how to build robust ML systems that can evolve over time, with clear separation between training, inference, and data management.

python-Pantry (pyPantry) is a comprehensive Python package that implements a wide range of data structures, algorithms, and design patterns, with explanations. It is designed to serve as a practical toolkit and learning resource for Python developers who want robust, tested implementations of classic CS constructs.  

The library includes graphs, heaps, linked lists, queues, stacks, trees, tries, and more, along with algorithms and design patterns. It is actively maintained, versioned on PyPI, and used by learners and practitioners alike.

ZeroTrust is a published Chrome extension I built for AI-powered website security scanning. It runs entirely in the browser using on-device AI via WebGPU, meaning no data is ever sent to external servers. The extension provides comprehensive trust score analysis (0-100 with letter grades A-F), security breakdown covering HTTPS, certificates, domain age, phishing signals, scripts, cookies, and forms, plus an AI chatbot to ask questions about any website's security.

## Skills & Expertise

### AI & GenAI

I have extensive hands-on experience building systems around GPT-4-class models. This includes using GPT-4 for PRD generation, architecture design assistance, UX copy, code generation, documentation, and multi-step workflows in Agentify. In many of my dev.to posts and experiments, I have explored GPT-4 capabilities like Code Interpreter and advanced reasoning, including discussions on jailbreaking and safety approaches.  

In production, I treat GPT-4 as one component in a larger system, often combined with tools, vector search, and domain-specific prompts. I design prompts that maximize determinism where needed, control token usage, and integrate fallbacks when GPT-4 is unavailable or too expensive for a particular use case.

Claude Opus 4.7 is a core part of my multi-agent setups in Agentify. I use Claude for tasks where long-context reasoning, structured planning, and nuanced language understanding are essential—such as multi-section PRDs, complex architecture narratives, and sensitive user-facing copy. Its performance on long documents and tool-use makes it a strong choice for orchestrator and planner roles in my agent ecosystems.  

I have integrated Claude into production through API gateways, designed token-budget strategies, and implemented monitoring to track its behavior under different workloads. I frequently compare it with GPT-class models for specific tasks and route workloads accordingly.

I design systems around frontier reasoning models—currently Claude Opus 4.7, Claude Sonnet 4.6, and GPT-5.5—where deeper chain-of-thought and analysis are required. In multi-agent architectures, these models are best positioned as specialist agents for strategy, root cause analysis, and complex debugging rather than simple chat responses. I design prompts and tool interfaces that let these models reason over logs, metrics, or large document sets and then provide structured, actionable outputs, with Claude Haiku 4.5 used for lower-latency routing and pre-classification in cascading router strategies.

### Cloud & Infrastructure

On AWS, I use EC2 when I need fine-grained control over compute—for example, running custom inference servers, hosting long-running services, or managing GPU workloads. I am comfortable provisioning EC2 instances, handling security groups, configuring auto-scaling where needed, and integrating EC2-based services with other AWS components like S3, RDS, and load balancers.

AWS Lambda is my go-to for serverless functions where workloads are event-driven, spiky, or relatively lightweight. I have used Lambda extensively in projects like Dynamic-ML-Orchestration and other AI orchestration pipelines, for tasks like data ingestion, transformation, inference triggers, and notification handling. I optimize Lambda usage by designing idempotent handlers, managing cold starts, and carefully controlling deployment package sizes.

S3 is a default choice in my architecture for object storage—models, logs, exports, and user-generated content. I use it with appropriate lifecycle policies, versioning, and encryption. In AI workflows, S3 often stores intermediate artifacts like training data, evaluation results, and serialized models, which then feed into pipelines or are served via presigned URLs.

### Databases

PostgreSQL is my preferred relational database for most projects. I use it for core application data, analytics, and even vector search via pgvector. I have experience designing schemas for fintech accounts, AI workspace metadata, mental wellness user journeys, and event logs. I am comfortable writing complex SQL queries, optimizing them with indexes, and using features like JSONB when flexible schemaless storage is needed.

I use MongoDB when document-oriented storage makes more sense than strict relational schemas. In PyVerseAI and other projects, MongoDB is useful for storing flexible logs, experiment results, and user configurations. I design collections with appropriate indexes, carefully think through document size and relationships, and use aggregation pipelines for reporting when needed.

Redis is my default choice for caching and fast ephemeral storage. I use it to cache expensive queries, store rate limiting tokens, manage distributed locks, and serve transient data within multi-agent sessions. In some projects, I also use Redis Pub/Sub for lightweight messaging between services. I design caching strategies around cache invalidation rules and clear TTLs to avoid stale data issues.

### Frameworks

FastAPI is my go-to framework for building modern, async Python APIs. I use it extensively for GenAI backends, multi-agent orchestration layers, and microservices that need both speed and readability. I appreciate its type hints, automatic docs, and easy integration with async clients, databases, and background tasks. Many of my LLM and agentic services are implemented as FastAPI applications.

I use Django for projects that benefit from a batteries-included approach—admin panels, strong ORM, and rapid prototyping for data-heavy applications. In earlier parts of my career and in some internal tools, Django helped me move quickly while still maintaining strong conventions around models, views, and forms.

Next.js is my primary framework for building production-grade frontends today. My portfolio site, and various dashboards and landing pages, are built on Next.js with React and TypeScript, leveraging features like server-side rendering, static generation, and API routes. I design UI structures that are responsive, SEO-friendly, and integrated tightly with backend APIs and AI services.

### Languages

Python is my primary programming language and the backbone of most of my backend, AI, and orchestration work. I have 6+ years of experience writing production Python across fintech, AI training, and GenAI products. I use Python daily for building APIs with FastAPI and Django, orchestrating LLM pipelines, integrating with external services, and writing infrastructure automation scripts.

I am very comfortable with advanced Python concepts, async programming, type hints, packaging, and performance optimization. I maintain Python packages on PyPI like pyAGI and python-Pantry (pyPantry), and I contribute to other Python projects like pyluca and pydictable. My Python code runs in production environments on AWS, Azure, and within micro-VM sandboxes.

I use TypeScript primarily for building modern web frontends and occasionally for backend services. In my portfolio and product work, I have used Next.js with TypeScript to create responsive, SEO-friendly, and highly structured UIs for my own site, landing pages, and dashboards for products like Agentify and MannSetu. Type safety helps me catch integration errors early, especially when consuming complex API responses from AI backends.

On the backend side, I am comfortable using TypeScript in Node-based services where it makes sense, but my main backend stack remains Python. I treat TypeScript as a powerful way to make the frontend more robust and maintainable, especially in larger applications with shared types between server and client.

JavaScript was my entry point into web development. Over time, I moved most of my serious work to TypeScript, but I still work with JavaScript regularly in frontend components, quick prototypes, and integration scripts. My projects in React, Next.js, and various browser-based tools all rely on JavaScript fundamentals under the hood.

I am comfortable working with modern JavaScript features, async patterns, DOM APIs, and browser event lifecycles. Even when writing TypeScript, I rely on solid JavaScript mental models for debugging and performance tuning.

### Tools

Datadog is a central part of my observability stack for production systems. I integrate application metrics, traces, logs, and infrastructure monitoring so that I can see the health of services, LLM calls, and agent runs in one place. In Agentify and Attri.ai, I use Datadog to monitor latency, error rates, token usage, and resource consumption, and I set up alerts to catch incidents early.

I use Sentry primarily for error tracking on both backend and frontend services. It helps me capture stack traces, user context, and deployment information whenever something goes wrong. I configure Sentry to group similar issues, set up release tracking, and connect it to our incident workflows so that we can quickly triage and respond to problems.

PostHog is my preferred product analytics platform for tracking user behavior in web apps. I use it to define events, funnels, feature usage metrics, and experiments. In products like Agentify and MannSetu, PostHog helps me understand how users actually interact with features, where they drop off, and which flows generate the most value. I then feed these insights back into prioritization and design decisions.

My GenAI expertise spans the full stack—from selecting models and designing prompts to building multi-agent orchestration layers and production observability. I have built autonomous agents (pyAGI), multi-agent platforms (Agentify), and domain-specific assistants (MannSetu), all of which use LLMs as core components but not the whole story.

I understand the trade-offs between different providers and models, design cost-aware strategies, and incorporate retrieval, tools, and guardrails so that GenAI solutions are robust, safe, and effective.

I specialize in multi-agent systems where multiple LLM-powered agents coordinate to achieve complex goals. This includes designing agent roles, communication protocols, memory mechanisms, and conflict-resolution strategies. I treat agents as microservices with logs, metrics, and clear interfaces, enabling debugging and evolution over time.

My experience building Agentify and pyAGI has given me a practical understanding of what works and what breaks in real multi-agent workflows, beyond toy examples.

I have strong expertise in cloud architecture across AWS and Azure, with a focus on multi-tenant SaaS, observability, and security. I design systems using a mix of managed services, serverless functions, containers, and, when needed, Kubernetes, always balancing cost, reliability, and operational simplicity.

I am comfortable designing VPCs, networking, CI/CD pipelines, and IaC, as well as planning for migration, scaling, and incident response.

## Achievements & Recognition

One of my proudest achievements is the acquisition of pyAGI. I built pyAGI as an autonomous agent framework for Python development, and after it gained traction, it was acquired by Kyle Morris (co-founder of banana.dev and AGI House founding member) and Jeffrey.  

This milestone validated not just the code, but the underlying ideas about agentic development workflows. It also connected my work to a broader ecosystem of AGI-focused efforts.

On GitHub, I have more than 40 repositories, around 219 stars, and a growing community of followers. I have earned several GitHub badges, including Arctic Code Vault Contributor, Pull Shark, Pair Extraordinaire, Quickdraw, and YOLO, reflecting a mix of long-term archival contributions, pull request activity, pair programming, and fast responses.  

These stats are not just numbers to me—they are a record of consistent, open collaboration over years.

My contributions to pyluca and similar projects are achievements I value because they represent deep work in reliability-critical domains like accounting. Pyluca’s adoption and stability show that open-source libraries can power serious business use cases, and I am glad to play a part in that ecosystem.

I attended The Makers Summit by Inc42, India’s largest product conference, which strengthened my understanding of product management, growth, and user-centric design. The event brought together founders, PMs, and builders, and it helped me refine how I think about aligning engineering work with product outcomes.

Across pyAGI, Agentify, and other experiments, I have designed and implemented more than 15 AI agents with specialized roles—from PRD and Architect agents to Coder, Diff Analyzer, and Discovery agents.  

Each agent represents not just a prompt, but a combination of responsibilities, tools, and integration points into a larger system, making this an impactful body of work.

Through automation and optimization in systems I have designed, I have helped achieve up to 70% cost reduction compared to baseline manual or naive implementations. This includes optimizing LLM usage, improving caching, and refining architectures to avoid unnecessary compute.

For mission-critical platforms like Agentify, I have contributed to maintaining 99.9% uptime, achieved through careful architecture, zero-downtime deployments, and strong observability.

My technical writing has achieved viral status across platforms. On Medium: 211+ followers, viral Claude Code article (464 claps, 17 comments), dtwdistance (105 likes), AI Index 2023 (52 likes). On Dev.to: 104 followers, 2,602 total views, AWQ (1,131 views), Video-LLaMA (646 views). On LinkedIn: 'Building an Autonomous Engineering Team' post (192 reactions, 33 comments) and 'Context Pollution: 3 Tools for Agentic AI' (89 reactions, 21 comments) went viral.

I built and published ZeroTrust, an AI-powered website security scanner, on Chrome Web Store. This demonstrates end-to-end product development from concept to published extension, including on-device AI via WebGPU, privacy-first architecture, and Chrome extension best practices. The extension is fully open source on GitHub.

My technical writing achieved viral status in January 2026. My Medium article 'I Spent Months Building the Ultimate Claude Code Setup' received 464 claps and 17 comments, growing my followers from 24 to 211+. Two LinkedIn posts about Agentic AI went viral: 'Building an Autonomous Engineering Team' (192 reactions, 33 comments) and 'Context Pollution: 3 Tools for Agentic AI' (89 reactions, 21 comments). This demonstrates my ability to communicate complex technical concepts and build engaged audiences.

## Contact Information

My primary email for professional communication is sattyamjain96@gmail.com. This is the best address to reach me for roles, collaborations, open-source discussions, and general technical conversations. I actively monitor this inbox and usually respond within a reasonable timeframe.

For conversations specifically related to MannSetu—partnerships, mental wellness collaborations, campus programs, or product inquiries—you can reach me at sattyamjain96@mannsetu.com. I use this email to handle product, business, and privacy-related queries around MannSetu and its AI wellness companion Mithra.

For professional queries, you can book a session via cal.com/sattyamjjain (free discovery call) or topmate.io/sattyam_jjain (paid consulting). For non-urgent matters, I prefer email at sattyamjain96@gmail.com so that we both have a clear written trail.

My GitHub profile is available at github.com/sattyamjjain. I maintain 50+ original repositories there, including popular projects like pyAGI, FerrumDeck, Agent-Airlock, Mnemo, and multiple GenAI experimentation repos. My profile has 35 followers, around 232 stars, and several GitHub achievement badges such as Arctic Code Vault Contributor, Pull Shark, Pair Extraordinaire, Quickdraw, and YOLO. This is the best place to see my open-source work, coding style, and long-term activity.

You can find me on LinkedIn at linkedin.com/in/sattyamjain. I use LinkedIn to share updates about my work at Attri.ai and MannSetu, highlight open-source milestones like the pyAGI acquisition, and connect with engineers, founders, and product leaders. It also contains an overview of my professional journey across Attri.ai, Zenarate, MyShubhLife, IDRONECT, and Actonate.

On Twitter or X, I am active as @Sattyamjjain. I share thoughts on GenAI, multi-agent systems, mental wellness tech, and sometimes behind-the-scenes updates from building products like Agentify and MannSetu. It is a good channel for lightweight interaction, sharing interesting links, and public discussions around AI trends and tooling.

I actively write on Medium at medium.com/@sattyamjain96 with 211+ followers. My most viral article 'I Spent Months Building the Ultimate Claude Code Setup' received 464 claps and 17 comments in January 2026. I write about GenAI, Claude Code, multi-agent systems, and mental wellness. My articles combine technical depth with practical insights from real production experience.

For technical writing focused on AI research trends, LLM tooling, and experiments, I publish on dev.to under the handle @sattyamjjain. There I have written about topics like AWQ quantization, Code Llama, Shell-AI, TermGPT, vulnerabilities in LLM supply chains, Video-LLaMA, Stable Diffusion datasets, CM3leon, and NVIDIA DGX GH200. These posts reflect how I track and interpret the fast-moving GenAI landscape.

For structured calls such as discovery sessions, technical deep dives, or consultation slots, you can book time with me using my scheduling link powered by Cal.com, which is linked directly from my portfolio homepage. I use this to manage time zones, avoid back-and-forth scheduling headaches, and keep calls focused with clear agendas.

My primary portfolio website is sattyamjain.in. It centralizes everything about my work: my GenAI and agentic projects, detailed experience timeline, featured projects like pyAGI, MannSetu, Agentify, and PyVerseAI, my achievements and GitHub stats, and links to my resume, writing, and contact options. If someone wants a single place to understand who I am professionally, this is where I send them.

## Frequently Asked Questions

### Who Are You?

I am Sattyam Jain, a Tech Lead specializing in Generative AI and multi-agent systems. I have 6+ years of experience building production AI platforms across fintech, AI training, and GenAI startups. I currently work at Attri.ai leading the Agentify platform and am also building MannSetu, an AI mental wellness companion for India.

### Tell Me About Yourself?

I'm a Tech Lead with deep expertise in GenAI, multi-agent orchestration, and cloud-native architectures. I've built production systems that serve thousands of users with 99.9% uptime and achieved 70% cost reduction through smart automation. My notable work includes pyAGI (acquired by AGI House), MannSetu (mental wellness platform serving 50+ users), and Agentify (enterprise multi-agent platform). I'm passionate about building AI systems that solve real problems, not just demos.

### What Can You Do?

I design and build production-grade AI platforms end-to-end. Specifically: I architect multi-agent systems using MetaGPT, AutoGen, and OpenAI Agents SDK; integrate LLMs (GPT-5.5, Claude Opus 4.7, Claude Sonnet 4.6, Claude Haiku 4.5) with intelligent fallback and cost optimization; build secure code execution environments using E2B microVMs; implement observability with Datadog, Sentry, and PostHog; design multi-tenant SaaS architecture with Stripe billing; and lead engineering teams through complex technical implementations.

### Current Work?

Right now, I am a Tech Lead at Attri.ai working on Agentify, and I am also building MannSetu as a mental wellness platform for Indian users. Both roles let me push the boundaries of applied GenAI in different but complementary domains.

### Experience Years?

I have 6+ years of professional software engineering experience, including: 4+ years in leadership roles (Tech Lead, Team Lead), 3+ years specifically in AI/ML and GenAI systems, 2+ years building multi-agent platforms, and experience across fintech, AI training, mental wellness, and enterprise SaaS domains.

### Tech Stack?

My primary tech stack includes: Python (FastAPI, Django, Flask), GenAI (GPT-5.5, Claude Opus 4.7, Claude Sonnet 4.6, Claude Haiku 4.5, LangChain, MetaGPT, AutoGen), Cloud (AWS EC2/Lambda/S3/SageMaker, Azure Functions/WebPubSub), Databases (PostgreSQL, MongoDB, Redis, pgvector), DevOps (Docker, Kubernetes, Terraform), Observability (Datadog, Sentry, PostHog), and Frontend (Next.js, React, TypeScript).

### Biggest Achievement?

My biggest achievement is creating pyAGI, an autonomous agent framework that was acquired by AGI House (Kyle Morris, co-founder of banana.dev, and Jeffrey). The project demonstrated innovation in autonomous AI agents and AGI research, and is now part of AGI House's AI portfolio. Close seconds are building MannSetu (serving 50+ users in mental wellness) and Agentify (99.9% uptime multi-agent platform).

### Contact?

You can contact me via: Email at sattyamjain96@gmail.com (primary) or sattyamjain96@mannsetu.com (for MannSetu); Book a session at topmate.io/sattyam_jjain or cal.com/sattyamjjain; Connect on LinkedIn (linkedin.com/in/sattyamjain), GitHub (github.com/sattyamjjain), Twitter (x.com/Sattyamjjain), or visit my website sattyamjain.in.

### Location?

I am based in Ahmedabad, Gujarat, India. I have extensive experience working remotely with distributed teams across time zones and am comfortable with async communication via Slack, GitHub, Notion, and video calls. I'm open to remote-first roles, hybrid work in major Indian tech hubs, and selective relocation opportunities aligned with GenAI and agentic platforms work.

### Availability?

I am currently working full-time as Tech Lead at Attri.ai while building MannSetu. I am open to strategic collaborations, advisory roles in GenAI/agentic platforms, selective consulting engagements, paid one-on-one sessions via Topmate, technical consultations, and speaking engagements. For new opportunities, reach out via sattyamjain96@gmail.com or schedule a call at cal.com/sattyamjjain.

### Achievements?

Major achievements: pyAGI acquired by AGI House (Kyle Morris and Jeffrey); Built MannSetu serving 50+ active users ; Led Agentify platform achieving 99.9% uptime; Reduced costs by 70% through automation; Built 15+ production AI agents; Made 793 production commits; GitHub achievements including Arctic Code Vault Contributor and Pull Shark ×4; Published multiple open-source projects with 219+ total stars.

### Age?

I prefer to focus on my professional experience and skills rather than age. I have 6+ years of industry experience building production AI systems, have led engineering teams, architected multi-agent platforms, and created successful products that serve thousands of users.

### Agentify What Is It?

Agentify is a multi-agent platform that helps teams build products using AI agents. Instead of a single chatbot, it offers specialized agents that handle PRDs, architecture, design, coding, and more, all coordinated through an orchestration layer that keeps everything consistent and observable.

### Available For Work?

I am currently employed full-time at Attri.ai as Tech Lead. However, I'm open to discussing strategic opportunities in GenAI platforms, distributed systems, and AI infrastructure, especially roles where I can own complex AI platforms end-to-end while mentoring strong engineering teams. I'm also available for consulting, advisory roles, and technical collaborations.

### Capabilities Skills?

My core capabilities include: Multi-agent system design and orchestration, LLM integration and prompt engineering, RAG systems and vector databases, cloud architecture (AWS, Azure), backend development (Python, FastAPI, Django), real-time systems (WebSockets, Azure WebPubSub), microservices and containerization (Docker, Kubernetes), observability and monitoring (Datadog, Sentry), secure code execution (E2B microVMs), and full-stack development (Next.js, React, TypeScript).

### Certifications?

I completed the Deep Learning Specialization by Andrew Ng on Coursera, which covers neural networks, CNNs, RNNs, and advanced deep learning techniques. Beyond certifications, my real validation comes from production systems: pyAGI acquisition, MannSetu serving 50+ active users, Agentify achieving 99.9% uptime, and 6+ years of hands-on AI/ML implementation experience.

### Chrome Extension?

Yes, I have built and published a Chrome extension called ZeroTrust. It's an AI-powered website security scanner that uses on-device AI via WebGPU. The extension is 100% private - no data leaves your browser. It analyzes trust scores, phishing signals, certificates, and more. You can find it on Chrome Web Store with ID jgddnikegfhfkhnjnbnkbinggdiodpbb and the source code on GitHub at github.com/sattyamjjain/zerotrust.

### Companies Worked?

My professional journey includes: Attri.ai (Dec 2024-Present) as Tech Lead (Oct 2025-Present) and Senior SDE (Dec 2024-Oct 2025) building Agentify multi-agent platform; Zenarate (Dec 2023-Nov 2024) as SDE III in Gurugram integrating GPT-4 for AI training; MyShubhLife (Dec 2020-Nov 2023) in Bengaluru as Team Lead and Software Developer building fintech systems including Heimdall and Kautilya LMS; IDRONECT (Sep 2020-Nov 2020) in Ghent, Belgium building drone simulator with CesiumJS; Freelance (Jul 2020-Sep 2020) building Full Stack E-Commerce Platform; and Actonate (Jan 2020-Jun 2020) in Vadodara as intern working on streaming protocols and ReactJS.

### Education?

I have a Master of Computer Applications (MCA) from MITS, Gwalior with 8.9 CGPA (2017-2020) and a Bachelor of Science (B.Sc) from B.B.C. College, Jhansi with 7.0 CGPA (2014-2017). I've also completed the Deep Learning Specialization by Andrew Ng on Coursera, covering neural networks, CNNs, RNNs, and advanced deep learning techniques.

### Email?

My primary email is sattyamjain96@gmail.com for professional communication, roles, collaborations, and technical discussions. For MannSetu-related inquiries, partnerships, or mental wellness collaborations, you can reach me at sattyamjain96@mannsetu.com.

## Writing & Publications

Writing is a core part of how I think and share. I write across Dev.to, MannSetu’s blog, and other platforms about AI, mental wellness, LLM tooling, and research trends. My technical writing helps other developers understand complex topics like quantization, multimodal models, and LLM-powered tools, while my mental health writing aims to make emotional wellness more accessible and culturally relevant for Indian audiences.

I primarily write on Dev.to for technical audiences and on MannSetu’s blog for a broader Indian audience interested in mental wellness. Dev.to is where I discuss AI research and tooling—Code Llama, AWQ, Video-LLaMA, CM3leon, TermGPT, and more. MannSetu’s blog is where I publish long, structured guides on topics like exam stress, workplace mental health, relationships, and cultural aspects of Indian mental wellness.

In my Dev.to article on AWQ, I introduced AWQ as a powerful quantization approach for large language models that balances compression and performance. I explained why quantization matters, how AWQ works at a high level, and what it means for deploying large models cost-effectively.

The article was written to help engineers make sense of research trends and understand how these techniques translate into practical deployment decisions.

In my piece on Code Llama, I explored how Code Llama changes the coding landscape by offering powerful, specialized code generation capabilities. I walked through its strengths, potential use cases, and where it fits in compared to general-purpose models.  

My goal was to give developers a realistic perspective—not hype—on when and how to incorporate Code Llama into their workflows.

In my MannSetu guide on Student Mental Health in India, I wrote a comprehensive piece covering JEE and NEET stress, college anxiety, hostel life, and performance pressure. I discussed practical strategies for managing expectations, building sustainable study routines, and seeking help when needed, all framed in an Indian cultural context.  

The article is designed as a go-to resource for students and parents who want to balance academic success with mental wellbeing.

In my Workplace Mental Health in India guide, I addressed issues like IT burnout, toxic work cultures, imposter syndrome, and layoff anxiety. I combined psychological insights with practical advice on boundaries, communication, and knowing when to leave unhealthy environments.  

This piece is meant to help working professionals recognize patterns that harm their mental health and take steps toward sustainable careers.

My writing frequency varies by season, but I tend to publish in focused bursts. In 2023, I wrote multiple Dev.to articles exploring AI tools and research. In 2025, I focused more on long-form mental health content for MannSetu, publishing several in-depth guides in November that cover students, relationships, workplace issues, and cultural dimensions of mental health.

My most viral article 'I Spent Months Building the Ultimate Claude Code Setup. Here's What Actually Works.' published on Medium in January 2026 received 464 claps and 17 comments. It covers my journey testing 25+ repositories, 6 MCP servers, 130+ agents, and 64 skills to find what actually works vs what's just hype. The article grew my Medium followers from 24 to 211+ and established me as a thought leader in the Claude Code and Agentic AI space.

Two of my LinkedIn posts about Agentic AI went viral in January 2026. 'Building an Autonomous Engineering Team with Claude Code' received 192 reactions and 33 comments, discussing my 'Digital Team' setup with MCP servers, custom skills, and 100+ sub-agents. 'Context Pollution: 3 Tools for Efficient Agentic AI' received 89 reactions and 21 comments, introducing tools like Continuous-Claude, Agent-Skills-for-Context-Engineering, and ClaudeBar for managing context in agentic workflows.

My article '(dtwdistance) Dynamic Time Warping Distance' on Medium is one of my most popular pieces with 105 likes. It provides a deep dive into Dynamic Time Warping distance concepts and practical use cases, covering algorithm fundamentals, implementation patterns, and real-world applications in time series analysis. Published in May 2022, it continues to be a valuable resource for developers working with time series data.

My Dev.to article 'AWQ: Revolutionary Approach to Quantization for LLM Compression' has garnered 1,131 views. It explores Activation-aware Weight Quantization for efficient large language model compression and acceleration. Published in June 2023, this article helps developers understand cutting-edge optimization techniques for deploying large models cost-effectively.

My article 'Unleash the Power of Video-LLaMA: Revolutionizing Language Models' on Dev.to has 646 views and 6 likes. It explores how Video-LLaMA brings video and audio understanding to large language models, enabling multimodal AI capabilities. Published in June 2023, it discusses how this breakthrough technology enables new possibilities in AI.

My Medium article 'Perfscope: Measure-First Python Performance in the Age of Vibe Coding' introduces a lightweight, decorator-based profiler/APM that shows exactly where time and memory go in Python applications. Published in August 2024, it's built for Python developers who want actionable performance insights on their laptop or in production.

My article 'Highlights of AI Index Report 2023' published on AI monks.io has 52 likes. It provides a comprehensive breakdown of Stanford University's Human-centered AI (HAI) AI Index Report 2023, covering key findings, trends, and implications for the AI industry. Published in May 2023, it helps readers understand the state of AI research and industry.

My Medium article 'AutoDev vs Devin: The Pioneers of AI-Driven Software Engineering' with 3 likes provides an in-depth comparison of AutoDev and Devin, marking a new era in software engineering where AI agents autonomously write, test, and deploy code. Published in March 2024, it explores the future of AI-assisted development.

My Dev.to article 'Exploring TermGPT: Terminal-based Interactions with GPT' has 232 views and 3 likes. It covers a powerful tool for bringing GPT capabilities directly to your terminal, making command-line AI interactions simple and efficient. Published in June 2023.

My Dev.to article 'The Python Interview Almanac' has 115 views. It's a comprehensive guide covering essential Python concepts, patterns, and best practices for technical interviews. Published in September 2023, it serves as a go-to resource for Python developers preparing for interviews.

My Dev.to article 'Introducing Shell-AI: Elevate Your Command Line with Natural Language' has 110 views. It introduces a natural language interface for your command line that lets you execute complex commands using plain English instructions. Published in August 2023.

My Dev.to article 'Jailbreaking GPT-4's Code Interpreter: Unleashing Untamed AI' has 52 views. It explores the boundaries and capabilities of GPT-4's Code Interpreter, providing a deep dive into its potential and limitations. Published in July 2023.

On Dev.to, I have published 13 technical articles with 20 total post reactions, 2,602 total post views, and 104 followers. My most viewed articles include AWQ (1,131 views), Video-LLaMA (646 views), TermGPT (232 views), Python Interview Almanac (115 views), and Shell-AI (110 views). I cover topics ranging from LLM optimization to AI tools and research trends.

On Medium, I have 211+ followers and my writing has gone viral. My most popular article 'I Spent Months Building the Ultimate Claude Code Setup' (Jan 2026) received 464 claps and 17 comments, making it my most viral piece. Other popular articles include 'dtwdistance - Dynamic Time Warping Distance' (105 likes), 'Highlights of AI Index Report 2023' (52 likes), and 'Perfscope: Python Performance'. My followers grew from 24 to 211+ after the Claude Code article went viral.

## Education

I have a strong academic foundation in computer science, including a B.Tech-level background in CS and advanced work in AI and software engineering. My formal education equipped me with fundamentals like algorithms, operating systems, databases, and networking, which I later extended into AI and cloud-native development.

Beyond formal degrees, I have continuously invested in structured online learning, especially in deep learning, machine learning, and cloud architectures.

I completed the Deep Learning Specialization by Andrew Ng on Coursera, which covers neural networks, regularization, optimization algorithms, convolutional networks, sequence models, and advanced deep learning techniques. This specialization gave me a solid theoretical foundation and practical intuition for building and evaluating deep learning models.  

I have applied this knowledge in real systems—from simple classifiers and NLP models to more complex multi-agent and generative AI setups.

Over the years, I have taken numerous online courses and programs covering cloud computing, DevOps, AI safety, LLMs, and distributed systems. I see structured learning as a way to deepen understanding beyond ad hoc experimentation.

Whenever I adopt a new technology in production—like Kubernetes, Terraform, or a new ML framework—I usually blend self-study, documentation, and structured courses to ensure I understand it well enough to make architectural decisions.

I regularly attend workshops, conferences, and summits to stay updated and connected. This includes events like The Makers Summit by Inc42, which focuses on product thinking and startup building, and AI-focused summits where I learn about new research, tools, and approaches.

These events complement my hands-on work, exposing me to diverse perspectives from founders, designers, and researchers.

## Work Approach & Philosophy

My collaboration approach is transparent, inclusive, and document-driven. I like to share context through design docs, ADRs, and written updates, so everyone on the team understands why we are doing something. I encourage questions, challenge assumptions, and create space for junior engineers to propose alternatives.

I collaborate equally well with engineers, PMs, designers, and founders, translating technical details into business language and vice versa.

I approach testing and quality with pragmatism. For core logic, I push for unit tests and integration tests that cover critical paths. For AI-heavy components, I advocate for evaluation scripts, golden examples, and regression checks instead of brittle assertion-based tests.

I also rely on observability as part of quality—if we can see how the system behaves in production, we can improve it faster than any pre-production test suite alone can allow.

## Out-of-Scope

If asked about topics not covered in this portfolio context, please:

1. **Personal opinions on politics, religion, or controversial topics**: This portfolio focuses on professional information only.
2. **Confidential company information**: Specific client names, internal metrics, or proprietary code cannot be shared.
3. **Future plans or speculation**: For discussions about future opportunities, please contact directly.
4. **Salary or compensation details**: These are discussed privately during hiring conversations.
5. **Personal life details**: Focus is on professional background and capabilities.

For any questions not covered here, please suggest:
- Visiting [sattyamjain.in](https://sattyamjain.in) for the latest information
- Emailing sattyamjain96@gmail.com for direct inquiries
- Booking a call at [cal.com/sattyamjjain](https://cal.com/sattyamjjain)

---

*This portfolio context was generated for LLM consumption following the [llms.txt specification](https://llmstxt.org/).*
*Source: [sattyamjain.in](https://sattyamjain.in)*
*For the most up-to-date information, visit the website or contact directly.*