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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Hire a Forward Deployed Engineer Who Gets AI to 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
When you need a forward deployed engineer, and when you don't
The forward deployed model fits a specific kind of problem: the AI part is possible, but the value depends on your systems. Your ticketing tool, your warehouse, your permission model, your compliance rules, the three people whose daily work changes when the agent goes live. A model vendor can't see any of that from outside, and neither can an agency working from a requirements doc. Someone has to sit inside the environment and do the integration work.
If your problem is mostly a model problem, like choosing between providers or tuning a prompt, you don't need an embedded engineer. A few hours of advice will do. Hire a forward deployed engineer when the problem is getting a working system through your infrastructure, your data and your people.
- Good fit: a pilot that worked in a demo and stalled before production
- Good fit: an agent that needs to read or act on internal systems, where nobody trusts it with production credentials yet
- Good fit: leadership wants AI shipped this quarter, and the in-house team is fully booked on the core product
- Not a fit: you want a strategy document with no build. Hire an advisor for that
- Not a fit: a standalone app with a fixed spec and no integration with your systems. A fixed-price build is cheaper
- Not a fit: the AI workload already fills five days a week, permanently. Hire full-time, and I can help write that spec
How a forward deployed engineer pilot runs
A pilot is scoped around one production outcome agreed in the first week, with a number attached. 'Explore AI for support' is not a pilot. 'Route 80% of inbound tickets correctly on a held-out set of last month's tickets' is. The rough shape:
- Week 1: in your repo, Slack and standups. Map the workflow with the people who do it, pull real examples, and agree the success metric and the eval set before writing agent code
- Week 2: first working slice in staging against your real data. Small, ugly and real
- Weeks 3–4: the work that separates a demo from production: evals in CI, permission scopes, audit logging, failure handling, a fallback when the model is down or wrong
- Week 5: limited production rollout behind a flag or a human-approval step, measured against the agreed number
- Week 6: decision point. Stop with a documented system your team owns, or continue on a fractional retainer for the next items on the backlog
What I need from your side
Most stalled AI projects stall on access and ownership, not on models. Before day one, I ask for:
- One named owner on your side who can make product calls the same day
- Repo access and a staging environment. Production access is earned later, with scoped credentials
- Real examples of the task: tickets, documents, queries or transcripts. A few hundred beats a perfect spec
- One success metric we both agree is the definition of done
- Time with the people whose workflow changes. They decide whether the system gets used
Shipped work that maps to forward deployed problems
Everything below is open source, so you can read the code before the scoping call. Each project is a pattern I reuse inside client environments.
- StellarMIND: an MCP server that turns plain-English questions into read-only SQL, using schema retrieval from pgvector. Only SELECT and WITH can run. That is the data-access pattern most internal agents need first
- Agent Autopsy: classifies why an agent run failed (loops, ignored errors, made-up tools, context bloat) from the raw transcript. I use the same discipline to rescue stalled pilots
- claude-autodev: an eight-stage pipeline where each stage only advances when it produces a real artifact, not when the model says it finished. I apply the same gating to production agents
- Quorum: a multi-agent research swarm that coordinates through a shared knowledge graph, with an explicit critic agent so the swarm doesn't just agree with itself
Forward deployed engineer vs AI consultant vs agency vs full-time hire
All four can be the right call. The difference is who builds, where the work lives, and who owns the system after launch.
| Forward deployed engineer | AI consultant | AI agency | Full-time AI hire | |
|---|---|---|---|---|
| Who builds | The same person who scoped it | Nobody. You get recommendations | An agency team, outside your context | Your employee |
| Where the work lives | Your repo, your Slack, your data | Slides and documents | Usually their repo, handed over at the end | Your repo |
| First working software | In staging within about two weeks | Not part of the deliverable | After a statement of work and a build phase | After the hiring search and ramp-up |
| After launch | Tunes it against real users, then a documented handoff | Engagement has ended | Support is a separate contract | Ongoing |
| Commitment | Fixed-scope pilot or part-week retainer | Project fee | Statement of work | Full-time headcount |
| Best when | Value depends on integrating with your systems and data | You have builders and need a second opinion | A well-specified build with no integration | AI work fills a full week, long-term |
Typical first pilots
Most first engagements take one of these shapes. Each is scoped so that 'production' is measurable before any code is written.
Support triage agent
Every inbound ticket is read by a person just to decide where it goes, and first-response time slips on busy days.
- Read tickets through your helpdesk API with a narrow, read-only tool layer
- Classify, route and draft a reply. A person approves before anything is sent
- Eval set built from a few hundred past tickets with known correct routing
Done means: routing accuracy on the held-out set meets the number agreed in week one, and the support team accepts drafts without rewriting them at an agreed rate.
Chat-to-SQL over internal data
Ops and finance keep asking engineers for the same queries, and the backlog of 'quick questions' never shrinks.
- Read-only database role with SELECT and WITH only, enforced in code and in the database
- Schema retrieval so the model sees the relevant tables, not the whole warehouse
- Query log and a verified-answer eval set of real questions
Done means: answers match the verified set, every query is logged, and no write path exists anywhere in the system.
MCP server for internal tools
The team wants Claude or an in-house agent to act on internal systems, but nobody will hand it production credentials.
- MCP server exposing a small set of named tools, each with its own permission scope
- Audit log of every call, rate limits, and staging first
- Runbook so your engineers can add tools without me
Done means: the agreed workflows run end to end through the server, every call is traceable, and your security review signs off.
Rescue a stalled pilot
The demo impressed everyone. With real traffic, the agent loops, ignores errors or answers confidently and wrongly.
- Replay real transcripts and classify each failure by cause
- Fix the top cause first, often retrieval or tool design rather than the prompt
- Turn every fixed failure into a regression eval that runs in CI
Done means: the failure rate on replayed traffic drops below the agreed threshold, and the eval suite blocks regressions on every change.
Tech Stack
Claude APIMCPAI AgentsRAGEvalsTypeScriptPythonAWS
Timeline
Fractional: ongoing, 2 days/week. Pilot: fixed scope.
Related Work
StellarMIND — Chat-to-SQL with RAG
Business users need to query databases without knowing SQL.
Agent Autopsy — Forensic Debugger for Failed AI Agent Runs
Most AI agent failures are silent — the run completes, the status code is green, and the result is still wrong.
claude-autodev — Autonomous 8-Stage Dev Pipeline for Claude Code
Agentic coding tools stop at a diff.
Quorum — Deep-Research Agent Swarm with a Shared GraphRAG Brain
Most AI research tools are one model in a loop.
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.
Related Engagements & Reading
Fractional Forward Deployed Engineer →
The same embedded model on a fixed-days-per-week retainer — how it works and when it beats a full-time hire.
What does a forward deployed engineer actually do? →
The role explained: origins at Palantir, why AI companies now hire for it, and the week-by-week reality.
MCP Integration Consultant →
Connecting agents to your internal systems is usually the first thing an FDE builds.
Claude Code Consultant →
Roll out agentic coding across your own engineering team while the AI system ships.
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.
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