Hire AI engineers who can ship on day one

Hiring a strong AI engineer directly is slow: sourcing, screening for genuine machine learning depth rather than API-wrapper familiarity, and closing a competitive candidate can take months, during which your AI roadmap sits idle. We maintain a pipeline of pre-vetted AI and ML engineers who can embed into your existing team on a contract basis, or move into a permanent role if that's the fit — evaluated for both technical depth and the ability to work inside someone else's codebase and process from week one.

We place AI engineers and machine learning specialists across the AI stack — from LLM application engineers who build on top of OpenAI and Anthropic Claude, to ML engineers who train and deploy custom models, to data scientists who turn data into decisions, to full-stack engineers comfortable shipping AI features end-to-end.

Sometimes one engineer isn't enough, but handing the work to an outside team isn't right either. A forward-deployed AI pod is a small embedded team — typically an AI engineer, a full-stack or data engineer, and a technical lead who owns delivery — that works inside your stack: your repositories, your cloud accounts, your ticketing, your standups. The pod brings the AI-specific practices many teams don't have yet, such as evaluation suites, prompt and model versioning, and cost monitoring, and leaves them behind in your codebase.

Pods fit well when you have a defined AI initiative — an agent, a retrieval system, an automation program — and want it built by people who sit with your team rather than behind a statement of work. The pod's size and mix change as the work does, and the goal is always transfer: your engineers pair with the pod, own the system by the end, and can hire from it if that's the fit. If you'd rather hand over a defined project entirely, see our AI agent development service instead; if you only need ongoing operation after launch, see managed AI services.

Every engineer we place is screened for technical depth in the specific area you need — not a generic coding test, but evaluation against the kind of problems your team actually faces, plus a check on communication and collaboration ability, since a senior engineer who can't work with your existing team and codebase isn't a fit regardless of raw skill.

We support both models. Contract staff augmentation lets you scale a team up quickly for a defined initiative without a long-term headcount commitment, while permanent placement is built for roles you know you need on your team indefinitely. Many engagements start as contract and convert to permanent once both sides confirm the fit — we structure the arrangement around what you actually need rather than pushing one model by default.

Every engagement starts with a free discovery call, where we learn your goals, systems, and constraints before proposing a scope. Tell us the role, the tech stack, and the timeline, and we'll share vetted candidates rather than a stack of resumes to screen yourself.