The AI Hire You Can't Make, Embedded Next Week
A senior AI engineer in your repo, your standup, your sprint board.
The forward deployed model Palantir named and OpenAI now runs at enterprise scale, sized for the companies those firms will never call.
The agent, built in your stack
Evals that catch a bad change
Identity, audit trail, human gates
How to run it without us
Not a body you rent. Output in your repo, under your review rules.
You Have Three Ways to Get AI Talent. All Three Are Broken.
You already know this, which is probably why the roadmap has an agent on it and nobody assigned to build it.
Hire an AI lead
Six months to find one. North of $300K fully loaded. One person, one skill set, no backup. If they leave, the knowledge leaves.
We cost about the same. The difference is who stands behind the desk, and how fast it starts.
Hire a big consultancy
A strategy deck, a rotating cast, and an invoice that reads like a phone book. The people writing the slides are not the people writing the code.
Ask your current team
Your engineers are good. They are also fully booked. Evals, guardrails, drift and cost control are a specialty, and learning it live on a deadline is how pilots stall.
What Is a Forward Deployed AI Engineer?
A senior engineer who embeds in your company and builds working software in your environment, instead of advising from outside it.
Where
Inside your team. Your repo, your standups, your sprint board, your Slack.
What
Production code, not recommendations. Agent workflows, evals, guardrails, and the data plumbing underneath.
Who
Palantir named the role. In May 2026 OpenAI spun up a dedicated deployment business around it and acquired a firm to staff it with about 150 forward deployed engineers. Databricks runs its own forward deployed org.
The trap
Every one of those runs it for the Fortune 500. At $50M to $1B you are not on the list, and that gap is the entire reason this offer exists.
Same model, mid-market economics.Every Resident arrives with Lumynate behind them: reusable agent patterns, an eval harness, and the guardrail stack, refined across every system we have shipped since 2010.
Bring us one workflow. We prove it pays, build it properly, control it so security signs off, and keep it running after everyone else has moved on.
The goal is a team that no longer needs us. Pairing is the model, not an add-on.
One Engineer or a Full Pod. Both Ship.
Pick by what you are missing. Specialty, or a whole delivery team with a date.
Resident
One senior AI engineer, embedded in your team.
From $25K/mo
Three-month minimum, then month to month with 30 days notice.
- Joins your standups, your Slack, your sprint board
- Builds in your repo. You own every line
- Escalates to RTS data, platform and security engineers at no extra cost
- Your team reviews their pull requests, not the other way round
- If they leave RTS we replace them and do not bill the ramp. Nothing lives on their laptop
- Pairs with your engineers so the capability stays
Best for adding agents to a product or platform you already run.
Agent Pod
A two to four person team for a defined agentic build.
$120K to $250K
fixed scope, fixed ship date
Converts to a Resident or to AgentOps after launch. Where you land in the band is team size and weeks, both settled before you sign.
- FDE lead, data engineer, fractional architect and QA
- A ship date in the contract, not in a roadmap deck
- Guardrails included: agent identity, audit trails, human gates, kill switches
- Ninety days of run support after go-live
- Agent two, same patterns, at 60% of agent one’s price. The first one pays for learning your systems. The second reuses that, and the library, and the evals
Best for taking one agent from working demo to governed production.
Alex Hogancamp, Director of Delivery, AI.
Picks who embeds with you and stays accountable for what they ship. Your first call is with an engineer, not a salesperson. Alex sits in on the first call and picks the Resident after it, not before, because the fit that matters is with your team and not with the job description.
Do the Arithmetic on a Year, Not a Month
A hire looks cheaper per month until you count the months you spend not having one. Here is the same year, both ways.
| Year one | Hire a senior AI engineer | RTS Resident |
|---|---|---|
| Time before any work happens | Six months of searching | Days |
| Recruiter fee | About $44K, at 20% of base | None |
| Cash out, year one | About $194K | $300K |
| Months of senior output | Six | Twelve |
| Cost per month of actual work | About $32K | $25K |
Embedded in Days. Shipping in Weeks.
Embed and map
Your Resident joins the team, reads the codebase, maps the data, and picks the first target with you. No discovery phase invoiced as a project.
First working ship
Something real runs in your environment. Small on purpose. Momentum beats a master plan.
Production, and your team ready
Governed, monitored, documented, handed off. Extend month to month, convert to AgentOps for the run layer, or stand down. Your call.
Embedded Team vs Hire vs Big Firm
| RTS Embedded | Full-time hire | Big consultancy | |
|---|---|---|---|
| Time to productive | Days | Three to six months | Weeks of workshops |
| Seniority | Always senior | One person, one bet | Partner sells, juniors build |
| Breadth behind them | A 100+ person firm behind one desk | None | Yes, at enterprise rates |
| Who owns the code | You | You | Read your MSA carefully |
| Guardrails and evals | Standard, via Lumynate | Depends on the hire | Sold separately |
| Walk-away cost | Thirty days notice | Severance, then restart | Change order |
| Knowledge transfer | Pairing is the model | Not applicable | Rarely priced in |
| Cost per month of real work | $25K | About $32K in year one | Higher, plus change orders |
What Shipping Looks Like
Named clients, published numbers, built by the same engineers who would embed with you.
- Stride: a multi-quarter database migration delivered in six weeks, zero downtime, 80% better query performance. The clearest read on what embedding does to a timeline.
- Landstar: 90% less time searching. One agent portal over three systems people were opening one at a time.
- Icon Custom Pools: quotes from one day to five minutes, and close rate up more than 40%. The agent assembles the price. A person still sends it.
- SkillCloud: three times the client capacity, 40% faster responses. Same team, same operating model, because the repetitive middle stopped needing a human.
More named work, workflow traces and the full stack live on our development page.
Embedded AI Teams, Answered
How is this different from staff augmentation?
Staff augmentation rents you a resume. An embedded engineer arrives with a delivery system behind them: reusable agent patterns, an eval harness, the guardrail stack, and a bench to escalate to.
The other difference is direction. Staff augmentation executes your plan. A Resident helps you form it, then executes it, and the stated goal is to make your team capable rather than to become permanent.
What does an embedded AI engineer actually do day to day?
Writes production code in your repo. A typical week is building agent workflows, wiring evals and monitoring, fixing the data plumbing that blocks the AI feature, reviewing your team’s pull requests, and pairing on the specialty skills: prompt architecture, guardrail design, cost control.
What they do not do is produce a deck. If you want a written recommendation before you commit engineering, that is the two-week diagnostic, not this.
We already started an AI pilot. Can an embedded engineer rescue it?
That is the most common starting point. Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, mostly for cost and governance reasons rather than model quality.
An embedded engineer works out whether yours is in that 40%, then either gets it to production or tells you plainly to stop spending. If you would rather have that verdict before committing to a monthly engagement, start with the two-week diagnostic instead.
What if the engineer is not a fit for our team?
Tell us in the first month and we replace them, and we do not bill for the ramp on the replacement. Embedding is a personality question as much as a skills question, and pretending otherwise helps nobody.
This is also why the first call is with the engineering lead who makes that assignment rather than with an account manager.
What happens if our Resident leaves, or takes two weeks off?
The same continuity you would want from your own team, which is the honest answer to the fair objection that one embedded engineer is also one person. Nothing lives on their laptop. The code, the evals, the runbook and the decision log are in your repo from day one, and a second RTS engineer is close enough to the work to cover.
If they leave RTS mid-engagement we replace them and do not bill the ramp. That is the part a direct hire cannot offer you.
Who reviews the code, and can we say no to an approach?
Your team reviews their pull requests and your standards apply. If your staff engineer rejects an approach, it does not ship. We are working inside your codebase, so your architecture decisions win by default.
This is the practical answer to being left with code nobody can maintain. The maintainability test is not a promise in a contract, it is whether your engineers approved every merge, and they do.
What is the minimum commitment?
Three months for a Resident, then month to month with thirty days notice. Pods are scoped to the project with the ship date in the contract.
Is this actually cheaper than hiring someone?
In year one, yes, and not because the monthly rate is low. It is because you do not spend six months searching, you do not pay a recruiter, and you get twelve months of senior output instead of six. That works out around $25K per month of real work against roughly $32K for the hire.
In year two it is a dead heat. The same money either way, except one of them comes with a bench behind it, no severance exposure, and thirty days notice. If you can fill the role in three months without a recruiter, hire. We will tell you so.
Who owns the IP?
You own everything built for you, in your repo. RTS keeps ownership of the pre-existing Lumynate core, the patterns, eval harness and governance tooling, which you license as part of the engagement.
Put plainly: your competitors funded none of your build, and you fund none of theirs.
Are your engineers US-based?
Yes, all of them. No offshore delivery and no subcontracted build teams. Relevant if you are in a regulated industry or your own contracts require it.
What if it works and my team cannot run it?
That is the more common problem, and it is why pairing is in the model rather than sold as an add-on. Your engineers review the Resident’s pull requests from week one, so what lands is code your own team has already read.
Where that is not enough you have two options, and neither is another hire. Keep a Resident on at a lower cadence for the run layer, or hand the run layer to AgentOps and keep the code. Both are month to month.
The answer that does not work is a runbook and good luck. A document is not an operator.
What happens when the engagement ends?
Your team keeps the code, the evals, the runbook and the monitoring, because all of it was built in your environment from day one. There is no export step and nothing to migrate off.
If you want the run layer handled rather than owned, that is AgentOps. If you want to stand down entirely, thirty days notice and we hand over.
Every Resident and every Pod is backed by Lumynate, our system for building and operating agents: the method, the reusable component library, the guardrails, and the team that keeps it running after go-live. It is the difference between hiring an engineer and hiring an engineer with sixteen years of shipped patterns behind them.
Know the Workflow. Need the Team?
Thirty minutes. Bring the stalled pilot, the roadmap, or just the problem. You leave with a plain read on whether embedding fits and what it would take.
Want a written verdict first? Start with the two-week diagnostic.