Building production-grade AI systems, retrieval pipelines, and scalable backend infrastructure.
- Production AI systems
- Retrieval-Augmented Generation (RAG)
- AI agents & orchestration
- Semantic retrieval pipelines
- Backend architecture
- Workflow automation
- Cloud-native deployments
- Scalable SaaS infrastructure
- LangChain
- LangGraph
- OpenAI APIs
- Gemini APIs
- RAG Pipelines
- Vector Search
- Semantic Retrieval
- Prompt Engineering
- FastAPI
- Flask
- PostgreSQL
- Redis
- Docker
- AWS
- CI/CD
- REST APIs
- Microservices
Production-grade multi-agent orchestration platform featuring:
- Dynamic worker routing
- Retrieval pipelines
- AI orchestration workflows
- Checkpoint persistence
- Evaluation pipelines
- Structured tracing
- FastAPI deployment architecture
Stack: Python, FastAPI, LangGraph, Gemini, SQLite
AI-powered backend platform combining:
- Semantic retrieval
- Vector search
- Deterministic tooling
- Operational analytics workflows
- Citation-aware RAG pipelines
- Production-style orchestration
Stack: FastAPI, ChromaDB, SQLite, AI Agents
High-performance semantic retrieval system featuring:
- FAISS vector search
- Retrieval benchmarking
- Query expansion
- Ranking optimization
- Recall@K / MRR / NDCG evaluation
- Retrieval observability workflows
Stack: Python, FastAPI, FAISS
- Shipping systems, not demos
- Building reliable AI workflows
- Clean backend architecture
- Production-ready engineering
- Solving operational problems with AI
- Built LLM-powered backend services and AI workflow APIs
- Developed RAG pipelines and semantic retrieval systems
- Integrated Dockerized AI services and CI/CD deployment workflows
- Developed enterprise backend workflow systems
- Implemented RBAC-driven operational modules
- Built Spring Boot microservices
- Developed backend orchestration and API validation workflows