Rohit delivered our MVP in 5 weeks — on budget and ahead of schedule. His architecture decisions saved us from rewriting everything when we scaled.
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AI That Actually Reaches Production — Not Another Stalled Pilot
A forward deployed engineer embeds inside your company, learns your workflows, and builds AI systems through to production. Not advice from the sidelines — working software in your repo.
The Problem
Your company knows AI should be doing more, but pilots keep dying in the demo stage. Agencies hand over a proof-of-concept and disappear. Advisory consultants produce slide decks, not systems. In-house engineers are busy keeping the product running. What's missing is one senior engineer who sits inside your context — your data, your compliance rules, your users — and owns an AI system from first prompt to production traffic. That is what a forward deployed engineer does, and hiring one full-time costs a senior-AI-hire salary you may not need yet.
What You Get
- ✓An AI consultant who works as a forward deployed engineer — embedded in your Slack, your standups, your codebase
- ✓Production AI systems: agents, RAG pipelines, LLM features, MCP tool integrations — shipped, monitored, and owned to stability
- ✓Two engagement models: fractional (2 days/week, ongoing) or a fixed-scope pilot with a defined production outcome
- ✓Evaluation harnesses before launch — every agent ships with evals, not vibes
- ✓Your stack, your repo, your IP — I work inside your GitHub from day one
- ✓Weekly written progress notes a non-technical founder can read
- ✓Handoff documentation so your team can run the system without me
Tech Stack
Claude APIMCPAI AgentsRAGEvalsTypeScriptPythonAWS
Timeline
Fractional: ongoing, 2 days/week. Pilot: fixed scope.
Related Work
Frequently Asked Questions
What does a forward deployed engineer actually do?
A forward deployed engineer (FDE) is a senior engineer placed directly inside a customer's environment to build and own a system through to production — the model Palantir pioneered and AI companies like OpenAI and Anthropic now hire for aggressively. Instead of receiving requirements and delivering a artifact months later, an FDE sits in your meetings, learns how your team actually works, finds where AI creates value, and ships working software inside your infrastructure. The role combines software engineering with domain understanding: less 'here is a recommendation', more 'here is the system, running, with your data'.
How is this different from hiring an AI consultant or an agency?
A traditional consultant advises — you still need someone to build. An agency builds — but from outside your context, usually handing off a demo that your team then struggles to productionize. A forward deployed engineer does both halves: consults on where AI fits, then personally builds it to production inside your environment. You get one accountable person, not a deck or a handoff.
What is a fractional forward deployed engineer?
Fractional means you get a fraction of my week — typically 2 focused days — on an ongoing retainer, instead of hiring a full-time senior AI engineer. You get the output that matters (agents built, LLM features shipped, integrations running) without the salary, hiring lag, or long-term commitment. Most companies' AI workload doesn't need five days a week; it needs two good ones, consistently.
Should I hire a freelance FDE or go fractional?
Freelance framing fits one-off, well-scoped tasks: a single integration, a proof-of-concept sprint. Fractional fits what AI work actually is: iterative, embedded, and ongoing — systems need tuning after they meet real users. I structure engagements as either a fixed-scope pilot (you want proof first) or a fractional retainer (you want sustained delivery). Both come with production ownership, not task completion.
How fast can you start, and what happens first?
First step is a scoping call — free, 30 minutes, NDA signed beforehand if you want. Within the first week of an engagement I'm in your repo and your Slack, and the first working slice ships within the first two weeks: something real in staging touching your actual data, not a slide about what could be built.
Testimonials
What Clients Say
We needed a WhatsApp bot for our clinic chain. Rohit understood the problem immediately and shipped a working solution that our staff could use without training.