Sattyam Jain

Tech Stack: Python, Azure, PostgreSQL, Django/Flask/FastAPI, Supabase, OpenAI (o1, GPT-4.5, o3, o4-mini), Claude, Microsoft AutoGen, MetaGPT, OpenAI Agents SDK, E2B, Datadog, Git, Jira

• Spearheaded engineering team of 6 engineers to architect and deploy Agentify—a production-grade multi-agent AI platform using MetaGPT, AutoGen, and OpenAI Agents SDK, achieving 99.9% uptime and serving 1,000+ active users
• Integrated latest OpenAI models (o1, GPT-4.5, o3, o4-mini) for real-time task automation and dynamic agent orchestration, improving response accuracy by 35%
• Architected secure code execution environment with E2B Firecracker microVMs achieving 100% isolation with sub-second cold starts, handling 10,000+ daily executions
• Built real-time communication layer with Supabase Auth, PostgreSQL, and WebSockets, enabling 15+ specialized agents (Orchestrator, PRD, Architect, Designer, Coder) to collaborate concurrently and reducing task completion time by 50%
• Implemented comprehensive observability with Datadog APM, custom metrics, and distributed tracing across all microservices, reducing incident response time by 80% (from 2 hours to 24 minutes)
• Refactored and stabilized legacy system by identifying core bottlenecks, prioritizing fixes, and optimizing Azure cloud resources, cutting downtime by 30% and reducing operational costs by 25%

Tech Stack: Python, AWS, SQL, Flask/FastAPI, Microsoft CLU/LUIS, GPT-4, OpenAI, Jira, Git

• Pioneered GPT-4 integration for AI-driven call simulations leveraging advanced prompt engineering techniques for private and open-source LLMs, improving training effectiveness by 40% and reducing trainer workload by 30%
• Engineered production-grade prompt framework with A/B testing and automated quality gates, reducing LLM hallucinations by 60% and improving response accuracy to 94%
• Developed automated reporting and analytics system using Python and ML algorithms, reducing manual QA time from 72 hours to 10 minutes (70% reduction) and enabling real-time insights
• Enhanced predictive reports by integrating AI/ML solutions via Azure CLU (Conversational Language Understanding) and LUIS, increasing report accuracy by 15% and improving client decision-making speed by 10%
• Redesigned system architecture from monolithic to microservices using Docker and Kubernetes, cutting downtime by 30%, accelerating AI deployment by 25%, and improving deployment frequency by 5x

Tech Stack: Python, AWS, Java, ReactJS, YouTrack, Pandas, Git, Docker, OCR, NER, ML, FastAPI, TensorFlow, Tesseract, Jenkins, GitLab

• Led onboarding efforts for 12 partners across fintech ecosystem, reducing integration time from 8 weeks to 4 weeks (50% reduction) through automated partner onboarding platform built with FastAPI, React, and Docker
• Architected production-grade NER (Named Entity Recognition) and OCR pipeline using TensorFlow, Tesseract, and custom ML models for document analysis, achieving 40% accuracy boost and processing 10,000+ documents daily for automated KYC verification with 92% precision
• Developed Heimdall—a comprehensive operational monitoring system with real-time alerting, custom dashboards, and anomaly detection, reducing incident detection time by 75% and improving system reliability to 99.5% uptime
• Contributed to development of Kautilya LMS (Learning Management System) serving 500+ users with course management, video streaming, and assessment features, achieving 85% course completion rate and 4.5/5 user satisfaction score
• Championed intelligent document processing initiative reducing manual data entry by 85% through ML-powered extraction, saving 200+ hours monthly
• Established robust CI/CD pipelines using Jenkins and GitLab, improving deployment reliability from 70% to 98%

Tech Stack: ReactJS, NodeJS, CesiumJS, Python, WebRTC, Selenium, PyTest, GitLab, Docker

• Architected 3D drone simulator using CesiumJS and ReactJS for real-time flight path visualization and mission planning, enabling testing of 50+ drone scenarios without physical hardware
• Engineered real-time drone data processing pipeline with NodeJS and WebSockets, handling telemetry streams from 20+ concurrent drones with sub-100ms latency
• Developed comprehensive test automation framework using Selenium and PyTest, reducing regression testing time by 70% and improving test coverage to 85%

Tech Stack: Python, ElectronJS, ReactJS, Java, Selenium, Chrome Extensions, REST APIs, SQL

• Contributed to full stack development of Zoho CRM features using ReactJS, ElectronJS, and Java, optimizing streaming protocol implementations and reducing video buffer time by 40%
• Engineered automated testing framework for Zoho CRM using Selenium and Python, reducing manual testing effort by 60% and improving release cycle speed by 2x
• Developed Chrome extension with React for enhanced CRM workflow automation, and built robust data migration scripts handling 100,000+ records with zero data loss

Dynamic-ML-Orchestration (Open Source)

Serverless ML orchestration platform using AWS Lambda, Step Functions, and MLflow. Achieved ~60% cost reduction vs. always-on EC2 instances and 10x faster pipeline deployment.

ImageTrust - AI Detection System

LLM-powered system detecting AI-generated images using multi-model ensemble (CLIP, ResNet, GPT-4 Vision). Achieved 94% accuracy with sub-2-second inference time.

PyVerseAI (Open Source)

Comprehensive AI/ML library collection with unified interfaces for TensorFlow, PyTorch, and Scikit-learn. Includes pre-built pipelines and production-ready examples.