Hiring nowMLOps and AI platform engineering

MLOps Engineer

Also called Machine Learning Operations Engineer

An MLOps Engineer builds the pipeline that packages, tests and releases models in a repeatable way. It matters because releases become predictable, traceable and easy to reverse when something goes wrong.

What they do

They automate the steps between a trained model and a live one. That means packaging models and their dependencies into containers, storing versioned artefacts in a registry, and running validation checks before anything ships. They build CI/CD pipelines that run those checks the same way every time. They set up orchestration for training and release jobs, and they design rollback so a bad model can be pulled quickly. They also record which data, code and configuration produced each release. Much of the week goes on fixing flaky pipelines, speeding up slow ones and helping data scientists release without waiting on anyone.

What a strong hire delivers

In the first months, a strong hire gives the team a model delivery pipeline that works end to end, with rollback support. Releases can be reproduced from recorded inputs. Validation gates catch obvious regressions before production. Lead time from approved model to live model drops. Rollbacks have been tested, not just designed. Data scientists release without hand-holding, and the team can see what is running where.

How you can tell they are good

Ask them to walk through a release pipeline they built, from commit to production, and to explain where it failed. Strong people talk about reproducibility, artefact versioning and rollback they have actually used. They can say what they measured, such as lead time or rollback success. Be wary of answers that list tools without explaining decisions, or that skip validation and recovery.

Why it is hard to fill

The title is used loosely. Some MLOps Engineers are DevOps people who have only touched models briefly. Others are ML engineers who dislike infrastructure. The real hire sits across both and is scarce. CVs list the same tools whatever the depth of experience, so tool names tell you little. Candidates who have owned a release through a failure are the ones worth finding, and they are often busy and not looking.

Where to find them

Look first at platform engineering and DevOps teams that have supported machine learning workloads. Also look at ML engineers who ended up owning deployment at product companies, and at site reliability engineers who moved toward model serving. Good candidates come from AI-native startups, scale-ups with several models in production, and larger technology firms with internal ML platforms. Data engineers with strong release discipline are a smaller but real source.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an MLOps Engineer

  1. Describe a model release pipeline you built. What stages did it have and what did you change after it first failed?

    A strong answer names concrete stages such as packaging, validation, registry push and deployment, and a real failure with a specific fix. Worry if they only describe tools, or claim nothing ever went wrong.

  2. How do you make sure a model release can be reproduced months later?

    Look for versioning of code, data, configuration and environment, plus recorded lineage in a registry. Good answers admit where reproducibility is hard, such as non-deterministic training. Worry about answers that rely on naming conventions or memory.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for an MLOps Engineer to own how our models get from approved to live. You will build the pipelines that package, validate and release models reliably, and make sure any release can be reproduced or rolled back. You will work closely with our ML and platform teams.

What you will do

  • Build and maintain CI/CD pipelines for model packaging, validation and release
  • Manage model artefacts and versions in a registry with clear lineage
  • Design and test rollback for model and application releases
  • Set up orchestration for training and deployment jobs
  • Add validation gates that catch regressions before production
  • Monitor release health and respond to incidents
  • Help data scientists release models with minimal friction

What you will bring

  • Built and run a model or service delivery pipeline in production
  • Hands-on work with containers and orchestration
  • Experience with artefact or model registries and experiment tracking
  • Evidence of making releases reproducible
  • Experience handling production incidents and rollbacks
  • Comfortable working with ML engineers and data scientists

Nice to have

  • Experience with distributed training or serving systems
  • Background in platform engineering or site reliability
  • Experience defining release metrics such as lead time

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • MLOps and model lifecycleAdvancedVersion, release, monitor and retire models
  • Cloud and orchestrationAdvancedProvision and operate scalable compute services
  • Distributed systemsWorkingDesign reliable work across networked machines
  • Reliability and incident responseWorkingDetect failures, restore service and prevent recurrence

Other roles in mLOps and AI platform engineering

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…

Related roles in other families

Hiring nowAI Cluster Network EngineerAI data centre networks and facilitiesAn AI Cluster Network Engineer designs and runs the network that links accelerators during distributed training. A well-built fabric keeps expensive…Hiring nowAI Data Centre Power EngineerAI data centre networks and facilitiesAn AI Data Centre Power Engineer plans the electrical capacity and resilience of AI compute sites. They make sure GPU clusters get safe, reliable…Hiring nowAI Thermal Systems EngineerAI data centre networks and facilitiesAn AI Thermal Systems Engineer designs and validates the cooling for dense accelerator racks. Good cooling keeps expensive compute inside…Hiring nowAI Compute Scheduler EngineerAI data centre networks and facilitiesAn AI Compute Scheduler Engineer decides which model workloads get which cluster resources, and when. Good scheduling keeps expensive GPUs busy…

How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

← MLOps and AI platform engineeringAll new job families

Start a search

Hiring an MLOps Engineer?

Tell us the work. We find the people already doing it, whatever their job title says, and you meet them already interviewed for your role.