What I build

End-to-end Generative AI capabilities

From GenAI prototypes to production LLM systems — fine-tuning, RAG, MCP servers, and multi-agent orchestration in any framework.

GenAI Project Development & LLM Application Engineering

From prototype to production-grade generative AI systems.

Generative AI moves fast, and most GenAI projects stall between an impressive demo and a reliable product. I design and ship production-grade LLM applications end to end — model selection, prompt architecture, evaluation harnesses, guardrails, cost optimization, and observability — so your generative AI actually holds up under real users and real traffic.

Explore GenAI Development

Building RAG Systems — Retrieval-Augmented Generation

Ground your LLM in your data — accurate, cited, hallucination-free.

Retrieval-augmented generation is the most reliable way to make an LLM answer from your own knowledge base instead of making things up. I build RAG systems that go well beyond naive chunk-and-embed: hybrid search, semantic re-ranking, metadata filtering, and citation-grounded answers that hold up in high-stakes domains like finance, healthcare, and law.

Explore RAG Systems

Fine-Tuning LLMs — Custom Model Training & Adaptation

Teach an open model your domain, voice, and tasks.

When prompting and RAG aren't enough — you need consistent format, domain jargon, a specific voice, or lower inference cost — fine-tuning adapts an open-weight model to your exact task. I handle the full fine-tuning lifecycle: dataset curation, LoRA/QLoRA or full training, rigorous evaluation, and deployment, so you own a model tuned to your business.

Explore Fine-Tuning LLMs

Building MCP Servers — Model Context Protocol Development

Give any LLM secure, typed access to your tools and data.

The Model Context Protocol (MCP) is becoming the standard way to connect LLMs to real systems — databases, APIs, file systems, and internal tools. I build production MCP servers that expose your capabilities to Claude and other MCP-compatible clients with proper authentication, typed schemas, and observability, so your AI can actually do work instead of just talking about it.

Explore MCP Servers

Building Multi-Agent Systems with Any Framework

Orchestrate autonomous agents that plan, use tools, and collaborate.

Complex work rarely fits a single prompt. Multi-agent systems break a goal into specialized agents — planners, researchers, coders, reviewers — that collaborate, call tools, and verify each other's work. I design and build multi-agent systems in whatever framework fits your stack: LangChain and LangGraph, Microsoft AutoGen, Semantic Kernel, CrewAI, or Google's Agent Development Kit (ADK) — with the orchestration, memory, and guardrails that make them reliable in production.

Explore Multi-Agent Systems