Full-stack engineer specializing in production AI infrastructure. I build RAG systems, semantic search platforms, and real-time data pipelines that handle thousands of documents with sub-2-second response times.
Have made production AI applications serving real users:
- Document intelligence processing 10,000+ documents at 99.9% uptime
- Semantic search with hybrid BM25 + vector similarity across 200+ code repositories
- Real-time email indexing with multi-LLM orchestration and automatic fallback (OpenAI → Gemini → Claude)
Engineering challenges I solve:
- RAG architecture with pgvector and intelligent chunking strategies
- Multi-provider API orchestration with fallback handling and rate limit management
- Real-time WebSocket systems with Redis caching, achieving sub-2s query responses
- Type-safe full-stack applications with tRPC and end-to-end TypeScript
- CI/CD pipelines on AWS/Vercel with Docker containerization
AI & Backend
RAG Architecture • LangChain • OpenAI API • pgvector • Vector Embeddings • Semantic Search
Python (FastAPI) • Node.js • PostgreSQL • Redis • Multi-LLM Orchestration
Frontend & Full-Stack
TypeScript • React • Next.js • tRPC • WebSockets • Real-time Systems
Infrastructure
AWS (EC2, S3, Lambda) • Docker • Vercel Edge Runtime • CI/CD • GitHub Actions
Integrations
OAuth (Gmail API) • Payment Processing (Razorpay/Stripe) • Third-party API orchestration
- 500+ commits in 2025 (consistent shipping velocity)
- 99.9% uptime across production systems
- Sub-2 second response times at scale
- Processing 10K+ documents in production
- 80% reduction in developer onboarding time with AI documentation tools
Open to full-time remote roles at startups where I can own technical architecture and ship features that matter. Available with US/EU timezone flexibility.
Looking for an engineer who can architect AI systems, integrate complex APIs, and ship production features fast? Let's talk.