Question 1

What kind of AI projects do you work on?

Accepted Answer

I specialize in production-ready LLM applications — RAG and GraphRAG pipelines, agentic AI systems, context engineering, and intelligent document processing. I've shipped these in regulated pharma environments with full audit-trail compliance, so I'm comfortable with both cutting-edge AI and enterprise-grade reliability.

Question 2

Are you open to full-time roles?

Accepted Answer

Yes — I'm open to both freelance projects and the right full-time role, especially in AI/ML engineering, LLM infrastructure, or senior software engineering positions at product-driven companies. I'm particularly drawn to teams pushing the boundaries of agentic AI and developer tools.

Question 3

How long does it take to build an MVP?

Accepted Answer

Typically 4–8 weeks for a lean MVP, depending on scope. I focus on rapid prototyping with clean architecture that scales. We'll define clear milestones during our initial discussion so expectations are aligned from day one.

Question 4

What's your tech stack?

Accepted Answer

Python, TypeScript, Go, and modern AI frameworks (LangChain, LangGraph, OpenAI, Bedrock). For backend I work with FastAPI, PostgreSQL, Neo4j, and Redis. Frontend with Next.js and React. Infrastructure with AWS, Docker, and GitHub Actions.

Question 5

Can you integrate AI into an existing product?

Accepted Answer

Absolutely. I've done this extensively — adding RAG-powered search, building intelligent document extraction, creating chatbot systems, and designing agentic workflows that plug into existing business processes. I'll work with your existing stack.

Question 6

What if I'm non-technical?

Accepted Answer

I excel at translating complex AI trade-offs into clear decisions. At WhiteHat Engineering, I regularly communicated technical choices to non-technical pharma stakeholders. You'll always understand what's being built and why.