Pre-vetted ML Engineer talent, fully managed delivery, structurally outcome-based pricing via Delivery Units. Onboarded in days — not months. No hiring overhead.
A ML Engineer pod from AiDOOS is a pre-assembled execution unit — vetted talent, a delivery manager, and the tooling to ship outcomes against your roadmap. We handle vetting, onboarding, governance, and reporting. You review shipped work against milestones.
ML engineers at AiDOOS specialize in production machine learning — model deployment, MLOps, feature engineering, and integration of ML systems into product workflows. Specialists cover PyTorch, TensorFlow, scikit-learn, transformers, and modern LLM-integration patterns. Typical seniority: 5–10 years of engineering experience with at least 3 in production ML.
ML pods are composed for the engagement — model-building work pairs ML engineers with data engineers; production-deployment work pairs them with platform and backend specialists. AiDOOS distinguishes ML engineering (production-shipping) from data-science research; our pods are oriented toward the former.
AiDOOS maintains a pre-vetted bench. Kickoff happens after scope alignment — not after a 60–90 day hiring funnel.
Every pod ships with a delivery manager, code-review SLAs, integration with your GitHub / Jira / Monday, and milestone reporting. Outcomes are auditable.
Add or release ML Engineer talent without long-term commitments. Delivery Unit (DU) pricing means you only pay for shipped, accepted work.
Pods are composed for the engagement. ML Engineers on AiDOOS pods commonly work across these technology stacks — pick the stack-specific page for engagement-fit details.
Pods include ML Engineers with prior sector experience. Each industry page covers compliance posture and common engagement types.
Tell us the outcomes you want shipped. We'll come back with a pod composition, milestone plan, and a pricing proposal — usually within 48 hours.