Senior AI engineers, embedded in your team, productive from the first sprint.
We place vetted AI, ML, and GenAI engineers directly inside your team — named specialists, not a rotating bench. They sit in your standups, ship in your repo, and answer to a single delivery lead.
You own every line of code and every model outright — embedded in one to two weeks.
Because the talent isn’t there to hire. AI has become the single hardest IT skill set to source — 45% of enterprise IT leaders name it the most prized and difficult to find. A senior ML or GenAI hire can take six to nine months to land, and the roadmap that needed them shipped on quarters, not quarters-plus-a-recruiting-cycle.
So the work stalls. The model that should be in production sits in a notebook; the agent your competitors are shipping waits on a req that won’t close. Meanwhile, the C-suite’s own diagnosis is the talent gap itself: skills shortages are the leading reason organizations say their generative-AI initiatives move too slowly.
AI staff augmentation closes that gap without the hire — experienced engineers inside your team now, instead of a search that ends two roadmaps too late.
This isn’t a bench you rent by the hour. It’s specific senior roles dropped into specific gaps — for each, what they do, the benefit they produce, and how it plays out:
Takes models out of the notebook and into a deployed, monitored service: feature work, training, serving, and the unglamorous reliability engineering that decides whether a model survives contact with real traffic. Benefit — the model your team prototyped finally ships, instead of stalling at the “demo works, prod doesn’t” line.
Example: a data-science team with a promising churn model but no one to productionize it gets an engineer who wires it into the pipeline and monitoring in weeks — so the forecast starts driving decisions instead of sitting in a Jupyter file.
Builds the retrieval, agent, and evaluation machinery around large language models — the engineering that turns an impressive demo into something you can put in front of customers. Benefit — you get LLM expertise that’s genuinely scarce, without competing for it on the open market.
Example: a product team that has never shipped RAG gets an engineer who has done it repeatedly, so the team avoids the six-month learning curve and the hallucination-in-production incident that usually comes with it.
Owns the path to production: CI/CD for models, drift and cost monitoring, versioning, and rollback — the discipline that keeps a deployed model from silently degrading. Benefit — models stay reliable in production instead of decaying unwatched.
Example: a team shipping its first model gets monitoring and a rollback path in place from day one, so a data-drift problem surfaces on a dashboard rather than in a customer complaint.
Builds the ingestion, transformation, and feature pipelines the models depend on — the layer that determines whether the AI work has clean fuel. Benefit — your AI initiatives stop stalling on data plumbing.
Example: a team blocked because the training data lives in five disconnected systems gets a reliable feature pipeline, so the modelers spend their time modeling instead of wrangling CSVs.
A hand-picked unit — engineering, MLOps, and a delivery lead — that operates as an extension of your team under shared standards, not a set of individuals you have to coordinate. Benefit — you stand up an entire AI capability in weeks, with one throat to choke.
Example: a company with a mandate to “do AI” but no AI team gets a pod that delivers the first production use case while the in-house team is still being recruited — and trains that team as it joins.
The difference between augmentation that works and a contractor who bills hours is in the scope below.
We match engineers to your actual environment — languages, cloud, data shape, and the outcome you’re after — not to whoever is on the bench. You see the fit before anyone starts.
Every candidate is vetted for production engineering depth, not certificate count, and you interview and approve them before they join. You’re choosing teammates, not accepting a roster.
Engineers join your standups, your repo, your code standards, and your review process — operating inside your workflow rather than handing deliverables over a wall. Productive from the first sprint, not the first month.
Because we are an AI lab, the augmented engineers bring the delivery discipline with them — evaluation suites, drift and cost monitoring, and human-in-the-loop checks — so the AI they ship is measured, not just demoed.
One accountable lead owns the relationship and the engineers’ output — a single point of contact who answers for the work, not an account manager who routes tickets.
Scale from a single specialist to a full pod and back as the roadmap shifts. You own all code, models, and deliverables outright under work-for-hire assignment — no lock-in, no black box, no dependency on us to keep running.
What you get when you hire us
The same founder-led delivery model behind all our AI development work, shaped for embedding rather than building to a fixed scope.
You tell us the roles, the data, the stack, and the timeline; we define the AI/ML capability the work actually needs.
Output: a role spec & a clear definition of done
We propose vetted engineers fit to your environment; you interview and approve them.
Output: named engineers you chose, not a bench
Engineers join your standups, repo, and review process inside your access controls, productive from the first sprint.
Output: working teammates in 1–2 weeks
They ship against your roadmap with evaluation and monitoring built in, under a named delivery lead.
Output: production work, measured against scope
Ramp up, ramp down, or transition the work to your in-house team as the roadmap moves.
Output: a capability that flexes with you
Engagement terms are tied to outcomes, and full work-for-hire IP assignment is signed before anyone writes a line.
The case against rotating contractors is straightforward: AI work compounds, and people who leave take the context with them. Our model is the opposite — named engineers who stay long enough to own the system.
The clearest proof is BJ’s Restaurants, a 200+ location chain whose software our team has carried as an embedded partner for four-plus years — the same people, the same standards, holding the business at twice-a-week releases with zero critical defects sustained across that span. That is what continuity, not churn, buys.
The pattern repeats. Bridge Athletic has been a live product partnership since 2012 — twelve-plus years of the same team carrying one platform through modernization and re-platforming without it ever going offline, now used by USC, the LA Rams, and MLB and MLS teams. Embedded engineers who stay are how a platform survives that long.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement and every engineer we place.
A lab, not a staffing agency. Our engineers carry Responsible AI delivery discipline — evals, monitoring, human-in-the-loop — into your team, because building reliable AI is what the lab does. A body shop sends you hands; we send you the practice.
Named continuity, not a rotating bench. You get hand-picked engineers who stay and own the context — the antidote to the contractor churn that quietly resets AI projects to zero.
You interview, you approve, you own. Every engineer is your choice; all code and models are assigned to you outright. No lock-in and no dependency on us to keep the system running.
One accountable lead. A single delivery lead answers for the work — no account-manager layer, no diffused responsibility.
Flex without headcount. Scale specialists up or down as the roadmap moves, with no permanent req to justify and no severance to unwind.
ML and data engineers who work inside HIPAA-compliant architectures, embedding into teams where every model decision must be logged and auditable. Healthcare software →
GenAI and ML specialists for fraud detection and real-time decisioning, embedded where conservative, auditable behavior is non-negotiable. Fintech software →
Engineers for recommendation, dynamic pricing, and the data pipelines behind them, plugged into existing product teams.
What teams want to know before they put embedded AI engineers on the roadmap.
It’s embedding vetted senior AI, ML, and GenAI engineers directly into your existing team — they work in your standups, your repo, and your workflow under your direction, instead of taking work away as an outside vendor. You get the specialized capacity you can’t hire fast enough, without adding permanent headcount, and you own everything they produce.
Typically one to two weeks from scoping to a productive engineer in your sprint, because we match from a vetted pool rather than running a months-long search. Compare that to the six-to-nine-month cycle a senior AI hire usually takes, against a backdrop where AI is the hardest IT skill to source (IDC).
For production engineering depth, not certificate counts — can they take a model from research to a monitored, reliable production service. You then interview and approve every engineer before they join your team, so the final call on fit is always yours.
Augmentation fits when you have a team and a roadmap but lack specific AI capacity, and want to keep the work in-house. A fixed-scope build fits when you want a defined system delivered end to end. We’ll tell you honestly which one your situation calls for in the scoping call — and sometimes the answer is a pod now that trains your team to take over later.
Because the talent is scarce and slow to land — AI is now the most difficult IT skill set to source, and the skills shortage is the leading reason gen-AI work stalls (McKinsey, 2025). Augmentation gets the capability working now and flexes with the roadmap; you can still hire permanently in parallel, and our engineers train your hires as they arrive rather than competing with them.
Fully. They join your standups, your code reviews, and your tooling, follow your standards, and report through a named delivery lead — operating as members of your team, not a parallel vendor shipping over a wall. The whole model is built on continuity, not the rotating-contractor pattern that resets a project every few months.
You do, outright. All code, models, and deliverables transfer to you under full work-for-hire IP assignment signed before work begins — no lock-in, no black box, and no dependency on us to keep the system running. Keep us on to scale, or take the keys entirely.
Yes — that’s the core advantage over permanent headcount. Add a specialist when the roadmap demands it, ramp down when it doesn’t, or transition the work to your in-house team. Engagement terms are tied to outcomes and built to flex, with no permanent req to justify.
Thirty minutes · No pitch deck
Tell us the roles, the data, and the timeline — we’ll tell you which specialists fit, and you can interview vetted AI and ML engineers within days.