Mara Lindqvist
Senior AI Engineer
Add one specialist or assemble a complete engineering pod. Every introduction is backed by live technical evidence, advanced AI fluency, production judgment, collaboration performance, professional English, role-relevant behavioral evidence, and consent-based verification.
Vetted continuously. Introduced selectively. Embedded sustainably.
Re-scoring published examples
Watch it work — click anywhere to take over.
Whether you need one specialist, several engineers, or a complete pod, we begin with your outcomes, team, and working reality. Then we introduce only engineers who choose the opportunity and meet the evidence standard required for it.
Fit first. No shortcuts. Every introduction is earned.
Describe what you’re building and the capabilities you need. Our interactive agent helps turn that context into a clear, reviewable brief.
A talent partner contacts you within one business day to calibrate the roles, working model, budget, and success criteria. You receive a search plan, commercial range, and realistic introduction window.
Once you approve the brief and engagement terms, we begin outreach. For standard staff augmentation, there is no financial commitment until you select an engineer.
Engineers review the actual opportunity and decide whether to opt in. We confirm interest, capacity, and role-specific evidence before presenting anyone.
Review curated introductions, meet the strongest fits, and choose who joins your team. The engagement begins only after both sides agree.
The vetted network
These published profiles illustrate the patterns we vet for; they are not a shortlist and we do not promise these individuals will be contacted. Recruiters review each submitted position before posting or outreach, then invite and validate interested engineers.
Start a positionSenior AI Engineer
Principal AI Engineer
Senior Automation Engineer
Senior Data Scientist
Staff AI Engineer
Senior ML Engineer
Principal Automation Engineer
Senior AI Engineer
Senior AI Engineer
Senior AI engineers who take models from notebook to production — LLM apps, RAG, agents, and automation.
We do not assess engineers through pedigree, keywords, or a single coding test. We observe how they build, reason, communicate, and make decisions in realistic working conditions.
Establishes reusable baseline evidence and working preferences.
Confirms that the evidence, interest, availability, and constraints fit the actual client position.
Passing the network standard never enrolls an engineer in a client opportunity.
One sentence drafts your team brief and previews representative fit patterns. Nothing is published and no engineer is contacted until a talent partner reviews the scope with you.
The right engineer. The right environment. Performance built to last.