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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Fractional Forward Deployed Engineer — Senior AI Delivery on a Two-Day-a-Week Retainer
The forward deployed engineer model — embedded in your environment, accountable for production — without the full-time salary or the six-month search. Fixed days each week, your repo, your Slack, your outcomes.
The Problem
Forward deployed engineer became the fastest-growing title in tech because companies learned the hard way that AI value lives in the last mile: integration, data, permissions, evals, and someone who owns the system after launch. But hiring an FDE full-time means competing with model vendors for a scarce profile, waiting months, and paying for five days a week when most companies' AI workload fills two. Agencies rent you a rotating team that never learns your context. A fractional forward deployed engineer is the middle path: one senior engineer, fixed days, inside your operations, until the workload justifies a permanent hire — and then a clean handoff.
What You Get
- ✓A senior forward deployed engineer on fixed days each week — typically two — on a monthly retainer, not ad-hoc hours
- ✓Embedded ways of working: your Slack, your standups, your repo, your compliance rules, your data
- ✓Production ownership of what ships — agents, MCP integrations, RAG pipelines, LLM features — including the tuning after real users arrive
- ✓Evaluation harnesses on every system, so a non-technical stakeholder can see it working before launch
- ✓A prioritised AI backlog ordered by return, revisited monthly with your leadership
- ✓Written weekly progress notes readable by a founder, a CFO, or a board
- ✓No lock-in: documented handoff whenever you hire full-time, plus help writing the spec and interviewing
- ✓Option to start with a fixed-scope pilot and convert to the retainer once value is proven
Tech Stack
Claude APIMCPAI AgentsRAGEvalsTypeScriptPythonAWS
Timeline
Ongoing retainer, 2 fixed days/week. Minimum 3 months.
Related Work
Frequently Asked Questions
What is a fractional forward deployed engineer?
A forward deployed engineer is a senior engineer placed inside a customer's environment to build, deploy, and own a system through to production — the model Palantir made famous and AI companies now hire for aggressively. Fractional means you get that engineer for a fixed fraction of the week, typically two days, on an ongoing retainer instead of a full-time salary. The embedded, accountable part of the FDE model stays intact. What changes is the commitment: you buy consistent senior days rather than a headcount.
Fractional FDE vs fractional AI engineer — what's the difference?
Honestly, they overlap, and I offer both. The difference is where the work lives. A fractional AI engineer builds AI features into your product from a backlog — features, models, pipelines. A fractional forward deployed engineer works inside your operations: connecting agents to the systems your business already runs on, navigating your data and permission boundaries, sitting with the people whose workflow is changing, and owning the deployment. If the hard part is integration and adoption inside your environment, you want the FDE framing. If the hard part is shipping product features, you want the AI engineer framing.
Fractional vs freelance forward deployed engineer — which does my company need?
A freelance FDE engagement is task-shaped: a defined integration, a proof-of-concept, a deadline, then done. Fractional is outcome-shaped and continuous: the same engineer stays, the system gets tuned after contact with real users, and the backlog keeps moving. AI systems are rarely finished at launch — evaluation scores drift, edge cases appear, the business changes — so continuity is usually worth more than a one-off. If you genuinely have a single bounded task, I structure that as a fixed-scope pilot instead of pretending it is a retainer.
Fractional FDE vs hiring one in-house — the build-or-rent question?
Hire in-house when the AI workload reliably fills a full week, shapes your core roadmap, and you can win a scarce profile against the model vendors hiring for the same role. Go fractional when you need senior delivery now, the workload is two or three days a week, or you want to prove the model before committing headcount. Most companies I work with start fractional, see real systems ship within the first month, and either stay fractional or hire full-time onto a working codebase — which is a much easier hire than starting cold. I help with that hire when the time comes.
How do you avoid context switching across clients?
By keeping the client count small and the days fixed. I take a limited number of fractional engagements at once, each with named days, so you get whole focused days rather than sliced hours. Between days I stay reachable in your Slack for questions that unblock your team; deep work happens on your days. Weekly written notes mean nobody has to reconstruct where things stand.
What does the first month look like?
Week one I am in your repo, your Slack, and your standups, mapping where AI actually creates value against what your systems will allow. By week two something real is running in staging against your data — not a slide. Weeks three and four harden that first slice: evals, permissions, monitoring, a runbook. From then on the cadence is steady: ship, measure, tune, next item on the backlog.
Related Engagements & Reading
Hire a Forward Deployed Engineer →
The fixed-scope pilot version: one defined production outcome, proof before a retainer.
What does a forward deployed engineer actually do? →
The role explained for hiring managers: origins, responsibilities, and why it exploded in 2026.
Fractional AI Engineer →
The product-backlog version of a part-week AI retainer.
Hire a Fractional CTO in India →
If what you need is leadership over an existing team rather than an engineer who 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.