I build AI systems that replace real work, not demos.
- Agentic AI systems over enterprise data
- RAG pipelines built for production scale
- AI copilots teams actually rely on
- End-to-end systems from model → API → deployment
- LangChain • LangGraph • LlamaIndex
- RAG • Agentic AI • MCP workflows
- LoRA / PEFT fine-tuning
- Prompt engineering (multi-step, tool-driven)
- Azure OpenAI • AI Foundry • Copilot Studio
- Azure AI Search • vector + hybrid retrieval
- AKS deployments • scalable pipelines
- Redis • Kafka • Airflow
- CI/CD • monitoring • evaluation pipelines
- LangSmith • RAG evaluation
- Node.js • Next.js • FastAPI
- PySpark • Databricks • Azure Data Factory
- Delta Lake • data modeling
- Hybrid + semantic search
- If it breaks in prod, it was never good
- If it doesn’t scale, it’s a prototype
- If users don’t rely on it, it’s noise
If you're building something serious and need AI to deliver:
- Less experimentation
- More execution
- Systems that stick
⚡ Building AI systems that do the work, not just talk about it