Pre-vetted AI Engineer talent, fully managed delivery, outcome-based pricing. Onboarded in days — not months. No hiring overhead.
A AI 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.
AI Engineers at AiDOOS specialize in production-grade artificial intelligence systems — LLM integration, retrieval-augmented generation (RAG) pipelines, agent orchestration, fine-tuning workflows, and the application layer that turns model outputs into product features. Specialists cover OpenAI, Anthropic, and open-source model integration, vector databases (Pinecone, Weaviate, pgvector), prompt engineering, evaluation frameworks, and the operational realities of running LLM-powered features in production.
Typical seniority: 5+ years of engineering experience with at least 2 in production AI/LLM work. AI Engineers fit pods building AI-augmented product features, customer-facing chat / assistant experiences, and internal tooling that uses LLMs to automate knowledge work. The role is distinct from ML Engineer — AI Engineers focus on the application layer, ML Engineers focus on model training and deployment.
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 AI Engineer talent without long-term commitments. Outcome-based pricing means you only pay for shipped work.
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.