Senior GenAI Engineer · Agentic AI · Python & FastAPI
I build production LLM systems: RAG pipelines, AI agents, and scalable backends. 4+ years of experience shipping for startups, from 0 → 1 to scale.
- Design RAG pipelines: document parsing, hierarchical chunking, hybrid search, re-ranking
- Build agentic workflows with tool calling, multi-step planning, and MCP integrations
- Develop async Python / FastAPI backends with streaming, background workers, and job queues
- Run LLMOps in production: tracing, prompt management, evals, and cost tracking
- Architect event-driven, cloud-native systems on AWS with Kafka and Redis
- Reduced p99 latency by 35–40% across production services
- Cut infrastructure costs by 30% with Kubernetes autoscaling
- Built real-time AI voice systems with <200ms latency
- Maintained 99.9% uptime with Prometheus and Grafana observability
GenAI & LLMs: OpenAI, Claude, Gemini, Llama, Hugging Face, Prompt Engineering, Function Calling, Embeddings
Agentic AI: LangChain, LangGraph, LlamaIndex, CrewAI, OpenAI Agents SDK, MCP
RAG & Vector DBs: pgvector, Pinecone, Qdrant, ChromaDB, FAISS, LlamaParse, Hybrid Search, Re-ranking
LLMOps: Langfuse, LangSmith, RAGAS, Guardrails, OpenTelemetry
Backend: Python, FastAPI, Pydantic, SQLAlchemy, asyncio, Celery / arq, Node.js, TypeScript, NestJS
Data & Messaging: PostgreSQL, PostGIS, Redis, MongoDB, Kafka, WebSockets, gRPC
Cloud & DevOps: AWS (ECS, Lambda, S3, RDS, Bedrock), Docker, Kubernetes, Terraform, GitHub Actions
"Build systems that scale. Optimize what matters. Ship fast, but design for the future."