Engineering and infrastructure

MLOps and AI platform engineering

MLOps and AI platform engineering is the work of versioning, deploying and governing model and application releases. It gives AI companies a reliable, traceable route from a working experiment to something customers can depend on.

Many AI companies have models that work in a notebook and a release process held together by habit. As customers, auditors and internal teams ask what changed and who approved it, that gap shows. The hires are tricky because the titles blur. Candidates usually come from platform engineering, DevOps or ML engineering, and each brings a different blind spot. Platform checks also support business-risk approval. They do not replace it.

Hiring now 3Emerging 1

Roles in this family

4 roles

Hiring nowMLOps EngineerAn MLOps Engineer builds the pipeline that packages, tests and releases models in a repeatable way. It matters because releases become predictable…Hiring nowAI Platform EngineerAn AI Platform Engineer builds the shared services that let teams use models and build AI applications without each reinventing the plumbing. It…EmergingLLMOps EngineerAn LLMOps Engineer runs prompt, model and retrieval changes as controlled releases. It matters because language model features break quietly, and…Hiring nowModel Registry and Release EngineerA Model Registry and Release Engineer keeps every model artefact traceable and every deployment approved. It matters because a company that cannot…

Where the people come from

Platform engineering, DevOps or ML engineering

Not to be confused with. Business-risk approval is not replaced by platform checks.

Core skills

  • Distributed systems
  • MLOps and model lifecycle
  • Cloud and orchestration
  • Reliability and incident response

Where it is used

  • Cross-sector AI platforms
  • Enterprise software and developer tools

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