Hiring nowMLOps and AI platform engineering

AI Platform Engineer

Also called ML Platform Engineer

An AI Platform Engineer builds the shared services that let teams use models and build AI applications without each reinventing the plumbing. It matters because it cuts duplicated work, keeps access controlled and makes releases dependable.

What they do

The work is building and running internal services that give teams access to models and a consistent way to ship AI applications. That means designing internal platform APIs, handling tenancy so teams stay separated, and setting access and usage controls. Day to day, they maintain model registries, CI/CD pipelines, containers and orchestration. They watch availability, respond to incidents and write up what went wrong. They also talk to the teams using the platform, work out what slows them down, and decide what the platform should do next and what it should leave alone.

What a strong hire delivers

In the first months, a strong hire gets a working shared service in front of real teams, with clear access and usage controls. They replace at least one pattern of duplicated infrastructure with a common path. They set up basic reliability practices, such as monitoring and an incident routine. They leave behind documentation that teams actually follow, and an honest view of what to build next.

How you can tell they are good

Ask them to walk through a platform they built and who used it. Strong people explain tenancy choices, how they limited access and tracked usage, and what they removed as well as what they added. Ask about an outage they handled. Listen for calm detail and lessons applied. Be wary of tool lists with no account of adoption or trade-offs.

Why it is hard to fill

The title is used loosely. Some candidates are DevOps engineers who have never dealt with model lifecycles. Others are ML engineers who have never run a service other teams depend on. The real skill sits between the two: internal APIs, tenancy and reliability for AI workloads. Few people have done all of it, and those who have are usually settled in roles where the platform is already valued.

Where to find them

Look first at platform engineering and DevOps teams that have supported machine learning work. Strong candidates also come from ML engineering teams that ended up building shared tooling for colleagues. Typical employers are larger technology companies with many internal teams, scale-ups running several AI products, and teams at model providers that serve internal users. Titles include ML Platform Engineer, Platform Engineer and Infrastructure Engineer.

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 AI Platform Engineer

  1. Tell me about an internal platform you built. Who used it and how did you know it was working?

    A strong answer names the users, the problem and the measures, such as availability and adoption. They should mention feedback from teams. Worry if they describe only technology choices or cannot say who relied on it.

  2. How would you design tenancy for an internal model access service used by several teams?

    Good answers cover isolation, identity, quotas, usage tracking and the cost of getting it wrong. They should ask about the teams and data involved. Worry if they treat it as a single API key problem.

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

Job description you can post

We are looking for an AI Platform Engineer to build the shared services our teams use to access models and ship AI applications. You will make the common path the easy one, keep access controlled and keep the platform dependable as more teams rely on it.

What you will do

  • Design and run internal platform APIs for model access and application development
  • Build tenancy, access and usage controls for teams sharing the platform
  • Maintain CI/CD, container, orchestration and registry tooling for model and application releases
  • Monitor availability, respond to incidents and lead follow-up fixes
  • Work with internal teams to find friction and reduce duplicated infrastructure
  • Document the platform and support teams adopting it

What you will bring

  • Demonstrated experience building and running shared platform services
  • Strong grounding in distributed systems and cloud infrastructure
  • Experience with container orchestration and CI/CD for production systems
  • Familiarity with model lifecycle tooling such as registries and experiment tracking
  • A record of handling incidents and improving reliability
  • Clear communication with engineers who use what you build

Nice to have

  • Experience serving machine learning workloads at scale
  • Experience designing multi-tenant internal services
  • Experience with usage metering or cost attribution

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

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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.

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