MLOps / AI Platform Engineer
What the work looks like, the stacks behind the title, what each level means and the training that helps, then how interviews run and how to prepare for each step.
What an MLOps or AI platform engineer actually does
An engineer who builds the platform that other teams use to train, deploy and run models, from GPUs to model serving in production.
An MLOps or AI platform engineer builds and runs the platform that ML engineers, AI engineers and data scientists use every day. This platform covers the GPU infrastructure, the training jobs, the model registry and the serving of models in production.
The role sits between infrastructure and machine learning. Platform engineers automate the path from a trained model to production with CI/CD for models, watch the reliability and the latency of model serving, and plan GPU capacity with the teams who need it.
GPU costs are high, so a large part of the work is about cost and capacity. More and more teams also work with AI coding tools such as Claude Code, Cursor or GitHub Copilot, and expect engineers to use them with judgment.
- Deploying a new model version on the serving platform
- Fixing a latency or GPU memory problem in production
- A review of GPU usage and costs with the teams
- Improving a training or fine-tuning pipeline
- Code reviews, runbooks and documentation
The same title, very different stacks
Most MLOps and AI platform searches are defined first by the part of the platform the team owns, then by the ground every platform engineer shares.
What each level means in practice
Titles vary from one company to the next. What changes from one level to the next is the scope you own and the decisions you take.
- Works on the platform with a senior engineer
- Learns the infrastructure, the tools and the users
- Writes tested code and runbooks with review
- Builds and runs parts of the platform on their own
- Fixes problems in serving and training
- Works directly with ML and AI teams
- Designs platform components for many teams
- Takes choices on cost, capacity and reliability
- Mentors other engineers and leads code reviews
- Sets the architecture of the AI platform
- Leads choices on vendors, clusters and standards
- Writes the design documents others build on
The paths into the role, and what really counts
Companies look first at platforms you have built and that other teams use in production. Education and certifications help most early in a career, or to change field.
- Engineering school or master's in computer scienceThe most common path in France, and often expected by large companies.
- University degree in computer scienceThe most common path in the US, at bachelor's or master's level.
- DevOps or platform engineering firstMany platform engineers start in DevOps or platform roles before moving to ML.
- ML engineering firstML engineers who like infrastructure also move into the role.
Our view: for this role, a platform you built and can walk through, from the GPUs to the teams who use it, often says more than any certificate.
A conversation with a senior tech recruiter
Your first interview is with one of our senior tech recruiters, who knows the role, the stacks and the levels in this guide. The conversation covers your own work through real examples, and the technical exercises come later, with the company. Before the call, we send you the six themes we will cover so you can prepare your examples.
- What you owned yourselfThe platforms, clusters or pipelines you were responsible for, and your own part in the team's work.
- Your environment and its constraintsGPU budget, capacity, latency, security rules or the needs of ML teams: what shaped your technical choices.
- The decisions you madeWhich serving stack, which tools and how to share GPUs, the options you compared and the reasons for your choice.
- The impact of your workWhat changed thanks to your work, with numbers when you have them: GPU costs, latency, deployment time or incidents.
- How you work with other teamsYour work with ML engineers, AI engineers, data scientists and security, and how you settle a disagreement.
- What you want nextThe kind of platform, scale and scope you are looking for in your next role.
- Platforms other teams use
- GPU cost and capacity
- Reliability of model serving
The same ground for every candidate. For a given role, every candidate answers the same final questions, built with the hiring manager around what the role really needs.
Ready for the next steps. After our call, we tell you how the company runs its interviews, who you will meet at each step and what each step is meant to assess.
What the company will assess, step by step
Each company runs its own process, and we walk you through its steps before you start. MLOps processes often combine the steps below, and the depth expected at each step grows with your level.
- A coding exerciseLive coding or a take-home, often automation in Python or Go, or a small service with tests.
- A platform system designA platform to design, such as model serving for many teams, with GPUs, autoscaling, monitoring and costs.
- A troubleshooting scenarioA problem in production, such as a latency spike or GPUs that run out of memory, and how you find the cause.
- Meeting the manager and the teamHow you work, how you take decisions with others and what the team expects in your first months.
- ConfirmedYou solve the exercise cleanly, explain each choice in simple words and think about the teams who use the platform.
- SeniorYou lead the design, weigh cost, latency and reliability, and point out risks and limits before you are asked.
- Staff and PrincipalYou run most of the interview as a peer, think about the whole AI platform and connect the design to the needs of several teams.
How to prepare as an MLOps or AI platform engineer
Practical advice for each step of an MLOps process, from the way you introduce yourself to the questions you ask at the end of each interview.
Start with the platforms you built, the teams who use them and your part in them. Mention your stack in one sentence, then close with what you want next.
For each one, prepare the users, the scale, your decision, the options you set aside, the result and what you would change today. Say "we" for the team and "I" for your own part.
Interviewers often ask about scale and cost. Prepare the number of GPUs, their usage, the costs and how you reduced them.
In a platform system design, ask which teams use the platform, for which models and with which needs before you draw anything.
In a troubleshooting scenario, explain each check you make and why, from the metrics to the logs, before you propose a fix.
Rules on AI coding assistants differ from one company to the next, and sometimes from one step to the next. Ask before each step, and when they are allowed, explain what you ask the tool and how you check its output.
- Walk us through a platform you built, and who used it.
- How do you share GPUs between several teams?
- Model latency doubles after a release. What do you check?
- How do you reduce the cost of serving an LLM?
- Which teams use the platform today, and for which models?
- Which serving and training tools does the team use?
- How does the team plan GPU capacity and costs?
- Who is on call for the platform, and how often?
- How do platform and ML teams share decisions?
- How does the team use AI coding tools day to day?
Where MLOps and AI platform engineers come from, and where they go
The roles people most often hold before this one, and the ones they most often move to.