AI Engineers
Embed senior AI engineering capacity
Staff augmentation for teams that need production-minded AI engineers — not resume farming.
Roles & capabilities
AI / ML engineers
Models, evaluation, data pipelines, and deployment.
Full-stack AI product engineers
Apps, APIs, UX, and LLM integrations that users actually adopt.
Automation & systems
Workflows, agents, and integrations across your stack.
Engagement models
Embedded engineer
Join your squad for a defined period with clear ownership.
Squad boost
A small cross-functional pod for a milestone.
Advisory + build
Strategy guidance with hands-on delivery support.
How embedding works
- 1
Clarify
Goals, constraints, data, and what “done” means.
- 2
Design
Architecture and a scoped plan you can defend.
- 3
Build
Working increments with reviews, not slideware.
- 4
Handover
Docs, training, and a path to operate or extend.
FAQ
Remote or on-site?
Primarily remote with overlap hours; on-site by arrangement for key workshops.
How do you match engineers?
We match on stack, domain, and seniority — then introduce candidates before any commitment.
Need capacity this quarter?
Tell us the role, stack, and timeline — we’ll respond with fit and availability.