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
How the retainer works, week to week
A fractional engagement only works if the days are predictable. Interrupt-driven hours, where the engineer is half-available all week, produce half-finished work. So the structure is fixed from the start:
- Named days agreed up front, for example Tuesday and Thursday. Those days are yours, in your Slack and your standups
- Between days I'm reachable in Slack for anything that blocks your team. Deep work waits for the named days
- A written note every week: what shipped, what the evals say, what's next, what I need from you
- A monthly backlog review with whoever owns AI on your leadership team, re-ranked by return
- A quarterly check on three options: keep going, scale down, or hire full-time and hand over
Signs your AI workload fits two days a week
Most companies overestimate how many engineer-days AI work needs, and underestimate how much continuity it needs. You're probably a fractional fit if:
- You have three to six AI use cases worth doing, not thirty
- Each one depends on your internal systems, so the same context gets reused across them
- Shipped systems need tuning as real users find edge cases, but not every day
- Your engineers can own day-to-day operation once someone builds it properly and documents it
- You want proof that AI pays off before committing a senior headcount
A sample first quarter
Every retainer starts from a ranked backlog. The quarter usually looks like this:
- Month 1: the highest-return use case goes from discovery to a working slice in staging, with an eval set agreed with the people whose work it changes
- Month 2: that system goes to limited production with permissions, audit logging and monitoring. The second use case starts, reusing the integrations from the first
- Month 3: both systems are tuned against real usage. Runbooks are written so your team handles routine operation, and the backlog is re-ranked on what the first two taught us
Handing off to a full-time hire
The retainer is meant to end well. When the AI workload grows past two or three days a week, the right move is usually a full-time hire, and a fractional engineer makes that hire easier. The new engineer joins a working codebase with evals and runbooks instead of a blank page.
- I write the job spec from the real system, not a generic template
- I join the technical interviews if you want a second opinion
- Two weeks of overlap with pairing on the live systems
- Ownership of evals, runbooks and on-call moves to the new hire, with nothing left in my head
The engineering behind the retainer
The habits that keep a part-week engagement reliable come from open-source projects you can inspect. Agent Autopsy diagnoses failed agent runs from their transcripts. claude-autodev refuses to advance a stage without a real artifact. MarginChef computes the numbers deterministically and uses the model only to write the advice, with a fallback when the model is unavailable. The same rules apply to every client system: measure before you trust it, and make sure it fails loudly.
Fractional FDE vs full-time hire vs contractor vs fractional AI engineer
Fractional is not always the answer. Here is where each model fits.
| Fractional FDE | Full-time FDE hire | Freelance contractor | Fractional AI engineer | |
|---|---|---|---|---|
| Commitment | Fixed days each week, three-month minimum | Full-time headcount | Per task or per hour | Fixed days each week |
| Time to start | In your repo in the first week | Hiring search, then ramp-up | Fast, for a single task | In your repo in the first week |
| Context over time | Same engineer, compounding | Highest | Resets with each task | Same engineer, compounding |
| Owns production | Yes, including tuning after launch | Yes | Usually ends at delivery | Yes |
| Where the work lives | Your operations: integrations, data, permissions, adoption | Same as fractional, full-time | Whatever the task is | Your product backlog: features and pipelines |
| Best when | AI work is two or three days a week and tied to your systems | AI work fills a full week, long-term | You have one bounded task | The hard part is shipping product features |
Tech Stack
Claude APIMCPAI AgentsRAGEvalsTypeScriptPythonAWS
Timeline
Ongoing retainer, 2 fixed days/week. Minimum 3 months.
Related Work
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.
MarginChef — AI Agent That Finds a Restaurant's Margin Leaks
Restaurants run on 3-5% net margins while food costs are up roughly 35% since 2019, and most owners never see per-dish economics.
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
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