AI · Applied ML · Generative AI · MLOps
Applied AI engineers, ML researchers, MLOps practitioners, and generative-AI specialists — screened on the specific frameworks, foundation models, and production patterns that govern enterprise AI in 2026.
Why ApTask for ai & ml talent
01
A solid Jupyter notebook does not ship customer value. We screen for engineers who have stood up production AI systems — retrieval-augmented generation, agentic workflows, evals harnesses, model gateways, prompt versioning — and dealt with the failure modes those systems actually have.
02
OpenAI, Anthropic, Google, Mistral, Llama. Closed APIs, open weights, self-hosted inference. Our AI recruiters operate against the actual landscape of foundation-model deployment options — and screen for engineers who can credibly pick between them for the workload at hand.
03
There is a real difference between an applied ML engineer who has driven business metrics with classical regressors and a researcher who has published on novel architectures. We staff for the role you actually have — and we are explicit about which kind of engineer is right for the seat.
04
Bias evaluation, hallucination management, prompt-injection mitigation, model-governance documentation. Our screening includes responsible-AI fluency — the maturity to ship AI to production without putting your brand at risk.
What we screen for
Our AI/ML recruiters operate against the actual modern AI stack — not against decade-old ML hiring templates. Screening closes before submission and includes:
Engagement-model fit
Senior AI engineers and ML researchers usually engage through Strategic Workforce Staffing — direct hire, contract, or contract-to-hire. For defined-outcome generative-AI builds (a private LLM deployment, an agentic workflow, a domain-specific RAG system), Managed Solutions (SOW) is the right model.
Read about Strategic Workforce StaffingQuantified outcomes
Private LLM go-live · Fortune 500 retailer
Six-person team — two LLM engineers, two DevOps specialists, an MLSecOps lead, and a fractional PM — stood up a private inference cluster on the client’s VPC, instrumented retrieval-augmented generation, and shipped a graduated rollout plan.
Tier-1 customer queries automated
Same retailer engagement. Reduced average resolution latency from 9 minutes to 22 seconds on automated tickets — sustained through the first holiday quarter post go-live.
Annual operational savings
Estimated operational savings on the same engagement, validated through customer-service headcount rebalancing.
Common questions
Send us the role, the timeline, and the constraint you’re most stuck on. A vertical recruiter will respond inside 24 hours with a calibration slate and a written staffing thesis.
