Engineers who go into your real environment — your data, your stack, your constraints — and turn an AI plan into a working system. Discovery, data integration, retrieval and agent workflows, evals, security, deployment, and the handoff that makes it stick with your team.
For a hiring manager who isn't deep in AI tooling: this is the difference between a slide deck that says "we could use AI here" and a person who sits with your team, learns your systems, and ships something that works on day one in production — then keeps it working.
Maps what data and systems actually exist, what's reachable, and what a realistic first working version looks like — before writing a line of integration code.
Connects your data to retrieval (RAG), builds agent and automation workflows, and wires it to your APIs — on Azure AI Foundry/Microsoft Foundry, OpenAI-compatible services, or your existing cloud stack.
Tests for correctness and failure modes before go-live, builds in observability, and documents a runbook so your own team can own and extend the system after the engagement.
Pricing and margin for each model are treated as hypotheses pending owner approval — exact figures are confirmed on your intake call.
Full-time AI Engineer, Applied AI Engineer, or AI Platform Engineer hired onto your team. Technical screening and culture-fit assessment included, with a 90-day replacement guarantee.
Scale AI implementation capacity on demand for a defined period — model buildout, a RAG rollout, an agent pilot. Weekly billing, W2 or Corp-to-Corp.
Evaluate a Forward Deployed AI Engineer on a real engagement for 60–180 days before converting to a permanent hire. No additional placement fee on conversion.
Part-time, ongoing embedded capacity — the right fit when you need senior AI engineering judgment but not a full-time seat yet.
A scoped, milestone-driven engagement to take one AI initiative from plan to production — the classic "forward deployed" model pioneered by frontier AI vendors and adopted across the market in 2026.
A small, iRemote-Support-managed pod (e.g. a Forward Deployed AI Engineer + an LLMOps/MLOps Engineer) for organizations standing up an AI capability without hiring an internal team yet.
The same security-aware, compliance-ready discipline we use for government-contractor and healthcare IT staffing, applied to AI implementation roles.
We confirm the role(s), AI stack, data-sensitivity constraints, and engagement model on a short discovery call before sourcing begins.
Candidates are screened against the role taxonomy's skills matrix, with hands-on project evidence (portfolio, GitHub, or a described case study) required — not just a resume.
Any claimed certification (Microsoft, AWS, Google Cloud, or other vendor credentials) is checked for current validity before it's presented to you — not taken at face value.
You receive a shortlist with verified evidence summaries. We coordinate interviews and manage onboarding, including any access/security review your environment requires.
We never publish or imply unverified salary, placement, or performance figures — about candidates or about ourselves.
We scope what, if anything, a candidate needs to access during screening — no unnecessary exposure to your systems or data before an engagement starts.
Screening criteria are written against demonstrable skills and evidence, not proxies that would systematically exclude qualified non-traditional candidates.
A Forward Deployed AI Engineer works directly inside your environment — not just in a vendor demo — to connect your data and systems to AI services, build retrieval and agent workflows, and get a working implementation into production with your team. It is a hybrid of solutions architect, implementation consultant, and software engineer, focused on outcomes rather than a pre-built product.
This is a dedicated practice for AI implementation roles — Forward Deployed AI Engineer, Applied AI Engineer, AI Platform Engineer, RAG/Knowledge Engineer, LLMOps/MLOps Engineer, and related titles — layered on top of, not replacing, our existing general IT recruiting.
Direct hire, contract staffing, contract-to-hire, fractional/embedded AI engineer, project-based Forward Deployed Engineer engagements, and managed team/pod offerings.
No single certification is required. We prioritize hands-on implementation experience and verifiable project evidence first. Where a candidate holds a current credential — such as Microsoft's Azure AI Apps and Agents Developer Associate (exam AI-103, built on Microsoft Foundry/Azure AI Foundry), AWS Certified AI Practitioner, or Google Cloud Professional Machine Learning Engineer — we verify it and surface it, but certification is one signal among several, not a gate.
Identity-consistent work history, hands-on project evidence, and the current validity of any certification claimed. We do not publish unverified performance claims about any candidate or about our own results.
Tell us about the engagement and we'll reach out within 1 business day to discuss fit and begin sourcing.