I embed with your team and ship AI to production.

Fixed days each week — typically two — embedded with your team on an ongoing AI backlog. Senior delivery without the full-time salary.

One defined outcome — an agent, an MCP integration, an LLM feature — shipped to production with evals. Proof before commitment.

Problem: A regulated-space fintech education company needed its content and product engineering to run without a full-time team.

Built: Production Next.js platform on AWS with an autonomous daily content pipeline — an AI agent researches, writes, typechecks, and deploys a new article every day — plus a financial-diagnostic product and admin tooling.

Outcome: The platform publishes daily with zero manual effort, and new product features ship weekly alongside it. Operated continuously in production.

Problem: Field-service operations lose jobs to slow, manual scheduling — dispatch decisions need to happen in seconds, correctly.

Built: A vertical AI dispatch agent that triages incoming jobs, matches technicians, and drafts customer communication — gated by an evaluation suite that must pass before any response ships.

Outcome: Dispatch decisions that previously required a human coordinator are handled end-to-end by the agent, with evals as the safety net.

Problem: AI-written code is only useful if it survives review — most agent coding tools stop at 'generated', not 'merged'.

Built: An autonomous pipeline that takes a requirement through spec, implementation, self-review, and tests in an isolated git worktree — plus a self-evolving agent that grows a reusable skill library from each task.

Outcome: Requirements go in, reviewed and tested branches come out — demonstrating the agent architecture patterns I deploy for clients.

What does a forward deployed engineer do that a normal consultant doesn't?

A traditional consultant hands you recommendations; you still need someone to build them. A forward deployed engineer embeds in your team — your Slack, your repo, your standups — finds where AI creates value, and personally builds the system to production. One accountable person from 'where should AI help?' to 'it is live and monitored'.

How do engagements work?

Two models. Fractional: fixed days each week (typically two) on an ongoing retainer — suited to a continuing AI backlog. Fixed-scope pilot: one defined outcome shipped to production — suited to proving value before committing. Both start with a free 30-minute scoping call, NDA first if you prefer.

What do you actually build?

AI agents that do real work (triage, dispatch, research, content operations), MCP servers that connect agents to your internal systems safely, RAG and LLM features inside existing products, and Claude Code workflows that make your own engineers faster. Everything ships with an evaluation suite — systems prove they work before users meet them.

Where are you based and who do you work with?

Pune, India — working remote-first with companies worldwide. US and EU clients get real daily timezone overlap plus written async updates. Contracts and IP assignment are structured for international engagements; you own the code and the repo from day one.