EmergingMLOps and AI platform engineering

LLMOps Engineer

Also called GenAI Platform Engineer

An LLMOps Engineer runs prompt, model and retrieval changes as controlled releases. It matters because language model features break quietly, and this person makes sure changes are tested, traceable and easy to undo.

What they do

They build and run the release process for LLM-powered features. Prompts, model versions, retrieval settings and orchestration logic are all versioned together, so any release can be rebuilt and compared. They wire evaluation gates into CI/CD, so a change that makes answers worse is stopped before users see it. They manage registries, containers and deployment pipelines, set up tracing and monitoring, and keep rollback paths ready. When quality drops in production, they work out which change caused it and recover quickly. They also help product and research colleagues ship changes without bypassing the process.

What a strong hire delivers

In the first months, a strong hire gives the team one versioned workflow in which prompts, models and retrieval changes move through the same pipeline. Evaluation gates catch regressions before release. Rollback is tested and routine. Production tracing shows what changed and when. Engineers and researchers know how to ship a change, and releases stop depending on one person's memory.

How you can tell they are good

Ask them to walk through a release they ran for an LLM feature. Look for a clear account of what was versioned, how quality was measured before release, and what happened when something went wrong. Ask how they would test a prompt change. Strong people talk about evaluation sets, drift, cost and rollback. Weak ones talk only about tools.

Why it is hard to fill

The title is new and rarely appears on CVs. The work often sits inside platform, DevOps or ML engineering roles, so good people look ordinary on paper. Few have run LLM releases in production for long, and those who have are usually busy. Candidates tend to be strong on infrastructure or on model behaviour, rarely both. We screen for the overlap.

Where to find them

Look at platform engineers, DevOps and site reliability engineers who moved into AI work, and ML engineers who got tired of manual deployments. They sit in product companies shipping LLM features, in AI start-ups with a growing user base, and in internal platform teams at larger firms. Some are titled GenAI Platform Engineer, MLOps Engineer or simply backend 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 LLMOps Engineer

  1. Describe how you version prompts, models and retrieval settings so a release can be reproduced.

    A strong answer treats them as one release bundle, with registries, configuration in source control and recorded evaluation results. Worry if prompts live in dashboards or documents with no history, or if only model weights are versioned.

  2. What would your evaluation gate look like for a prompt change to a customer-facing assistant?

    Look for representative test sets, automated scoring with human spot checks, thresholds agreed with product, and attention to cost and latency. Worry about a single aggregate score, or gates that nobody trusts and so everyone bypasses.

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

Job description you can post

We are building products on language models and need someone to make our releases dependable. You will own how prompt, model and retrieval changes are tested, shipped and recovered, working with engineers, researchers and product colleagues so that we can move quickly without breaking quality for users.

What you will do

  • Build and run versioned release pipelines for LLM workflows
  • Add evaluation gates to CI/CD that block quality regressions
  • Manage model, prompt and index registries and their promotion paths
  • Set up tracing and monitoring for quality, cost and latency
  • Own rollback and incident response for LLM features
  • Help teams ship changes safely through shared tooling and documentation

What you will bring

  • Have run production deployments for ML or LLM-based systems
  • Can show versioning of models, prompts or data in a real project
  • Experience with CI/CD, containers and orchestration
  • Have built or used evaluation tests to gate releases
  • Have handled production incidents and written clear reviews
  • Can explain trade-offs to researchers and product colleagues

Nice to have

  • Experience with retrieval systems and index management
  • Experience with agent frameworks in production
  • Experience managing model provider changes and fallbacks

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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