Project 1 — Microservices-Based Hybrid Retrieval Platform with Distributed AI Document Ingestion

• Implemented MCP (Model Context Protocol) using Azure Function Apps with optimized real-time Flask SSE streaming on OpenShift; added an intelligent latency-aware caching layer cutting per-request setup time from 6s → 1.6s (73% faster), improving grounding & response latency by 50% over traditional RAG.
• Built a production-scale event-driven ingestion pipeline (Azure Functions, Service Bus, AI Search) with queue-based orchestration & stage-level retries, processing 1M+ documents while cutting pipeline cost by 25%.
• Optimized vector search with pre/post-filtering in Azure AI Search — +30% retrieval precision, -50% latency; built a Tesseract OCR pipeline with 90%+ accuracy.
• Upgraded GPT-4o mini → GPT-5.4 mini and ada-002 → text-embedding-3-large (1536D) for better accuracy & hallucination resistance.
• Engineered correlation ID-based distributed tracing & Dynatrace observability dashboards across microservices.

Python Azure Functions Event Grid Service Bus Microsoft Foundry Doc Intelligence RAG MCP Azure AI Search Tesseract OCR Flask (SSE) Gunicorn Dynatrace SonarQube OpenShift GitHub Copilot

Project 2 — GenAI Engineer: HPC Modernization (Fortran → Cloud)

• Migrated a legacy Fortran on-prem DLL engine to AWS Cloud, optimizing core modules in C++/Python for 2–3× scalability & 5–10% faster execution.
• Implemented OpenMP/MPI-style parallel processing while preserving benchmark parity with the legacy implementation.
• Migrated on-prem databases to AWS achieving 99.9% availability.
• Used Claude for code understanding & refactoring, cutting dev effort by 70%.

C++ Python (NumPy) AWS (EC2, Terraform, CI/CD, Redis, Load Balancer) OpenMP/MPI Snyk Claude Code

High-Performance Workfeed System

• Designed a scalable backend validated via Autocannon load testing, sustaining 50k+ concurrent users & 100k+ requests/min.
• Improved horizontal scalability with PostgreSQL sharding, read replicas, Redis caching & S3 storage — enabling 200× user growth.
• Built a video ingestion pipeline (multipart uploads, FFmpeg, AWS Lambda, MediaConvert) for adaptive encoding up to 4K.
• Implemented an API Gateway + event-driven microservices architecture for fault isolation & async processing.

React Native Node.js Supabase AWS (EC2, S3, Lambda, CloudFront, MediaConvert) Razorpay FFmpeg BullMQ/Redis NativeWind Docker Kubernetes

🔧 Work in progress: Context API → Redux migration, Supabase monolith → AWS microservices.