MLOps Engineer

The MLOps Engineer is a specialist in making machine learning models ready for production and operating them. They connect model development with production operation. Their work enables teams to repeat processing steps, deploy identifiable versions and monitor their behaviour once in use.

Their responsibilities cover pipelines, meaning sequences of processing steps, validation and the automation of production deployments. They also organise run traceability and model monitoring. A trained model alone therefore does not define their deliverable: teams must be able to trace the conditions behind a result, diagnose a discrepancy and prepare to replace a faulty version.

MLOps Engineer, ML Engineer and Data Engineer: who does what?

• The MLOps Engineer focuses on repeatable processing and model operation. Their work connects development choices with deployment constraints.
• The ML Engineer may combine model design, training and deployment. Their responsibilities may therefore overlap with those of the MLOps Engineer: clarify responsibilities rather than relying on the job title alone.
• To define how the role works with the Data Engineer, establish who prepares the data, who checks that it meets requirements as it enters ML pipelines and who intervenes when a change disrupts processing.

The division of responsibilities between model design and deployment varies by organisation.

This job profile covers machine learning operations broadly. Language models are a specific case with additional requirements, rather than the defining feature of every MLOps role.

The challenge is to keep operations manageable when data, code or models change. Automating processing and validation helps make production deployments repeatable. Traceability then makes it possible to identify the version used and the conditions of a run. Without these elements, a team has less information to explain why two results differ.

Monitoring must also cover model performance in use. A deterioration calls for diagnosis before choosing a response: raising an alert, reverting to a previous version or preparing to retrain the model. For a business leader, a key decision is to define who can initiate each of these actions and on what basis.

Hypothetical example: a team replaces a model, then notices a drop in prediction quality. If it can retrieve the data, code and artefacts associated with each run, it has points of comparison. Artefacts are the outputs produced by processing, such as the trained model. The team can examine the change and prepare a rollback. If it keeps only the latest model, it loses some of the information needed for diagnosis.

The way teams work together matters as much as automation. When a data change affects a pipeline, clarify how the MLOps Engineer works with the Data Engineer. Both teams must understand which check to carry out, whom to notify and who is responsible for the correction.

Poorly defining the role can mean looking only for a specialist in a particular tool when the need is to design validation steps and handle incidents. Conversely, adding more components does not define a workable operating arrangement. Start with the processing steps to repeat, the information to retain and the decisions to make. You can then choose the useful platform features and the level of autonomy expected of the person you hire.

• Automate pipelines that connect data preparation, training, validation and production deployment.
• Integrate data, code and model checks into automated processing.
• Version artefacts and track runs to trace the conditions behind a result.
• Prepare deployments and procedures for reverting to a previous version.
• Monitor data and model performance to identify deterioration.
• Configure retraining triggers according to the agreed events and needs.
• Document technical choices and coordinate responsibilities with the teams involved.

Training for the MLOps Engineer role aims to connect model training with reproducible processing, deployment and monitoring. Useful areas of learning include programming, validation, traceability and production deployment constraints. They combine an understanding of machine learning with the ability to build an operational pipeline.

The skills to develop depend on the person's starting point. A development background can be complemented by model training and validation. For someone coming from infrastructure or DevOps, further learning focuses on understanding data and model behaviour. For someone with a data background, it focuses on designing reusable code, testing and production deployment constraints. These routes help build on existing skills without guaranteeing direct entry into the profession.

The Microsoft Learn MLOps learning path provides an example of supplementary training: it draws in particular on Python or R programming, ML training and cloud platform fundamentals. These prerequisites apply to that specific learning path and do not make any language or provider mandatory for every role.

In France, the EPSI level 7 certification in data engineering includes configuring MLOps pipelines. It provides for initial admission or admission at a later stage, and takes professional and personal prior learning into account. It is one possible route, broader than MLOps alone, rather than a mandatory qualification.

GetPro's general method is based on a criteria grid, interviews and reference checks. The MLOps-specific guidance that follows offers advice for your recruitment.

The foundations of GetPro's assessment

The grid distinguishes criteria that can be verified from a candidate's background from those requiring a question or test. Each criterion has an assessment method. The grid directs interviews towards points that still need examining.

Interviews use open questions and concrete examples to examine the key criteria. Reference checks explore in greater depth the skills and points requiring attention identified during interviews. Questions place the working relationship in context and ask for concrete examples. A summary brings together the information useful to the decision.

Advice for your MLOps recruitment

1. Define the criteria and expected autonomy

Build your grid around the tasks the person will actually be given: automation, validation, traceability, deployment and diagnosis. For each criterion, distinguish between carrying out work with support, designing independently and coordinating with other teams.

Clarify the decisions the candidate will need to make and the help they will be able to seek. Ask them to describe their personal responsibilities in a team achievement. Do not judge their autonomy solely by the number of tools they mention.

2. Examine an end-to-end achievement

Ask the candidate to describe a model they helped put into production. Have them clarify their involvement, the validation steps added and the information retained to trace a run.

Where the candidate is able to share them, examine a pipeline diagram, a description of tests or a deployment procedure. Ask how the data, code and model versions were linked. Look for an explanation that makes it clear how to reproduce the processing. If the demonstration is limited to launching a tool, explore the choices made in more depth.

3. Present a diagnosis and rollback scenario

Hypothetical example: a model has just been replaced and prediction quality is falling. Ask the candidate what information they seek before acting, then how they prepare to restore a previous version.

Ask them to distinguish checks on the technical execution of processing from those concerning data and model performance. Ask under what conditions they would consider retraining.

Assess the order of investigation, the traceability they look for and their explanation of the limits of the diagnosis. Observe whether the candidate states their hypotheses and asks for missing information. Probe further if their answer is to trigger retraining immediately without examining the change.

4. Examine communication and coordination

Ask for an explanation of the diagnosis aimed at an HR or business stakeholder. Look for wording that makes the problem, the available information and the next decision clear.

Ask them to specify who needs to intervene if the input data has changed. For a coordination role, ask how the candidate would allocate validation tasks and document responsibilities. If the role includes management, examine their experience of supporting skills development separately.

5. Complete the information needed for your decision

During reference checks, ask for more detail on the responsibilities already described: personal contribution, decision-making autonomy and collaboration when difficulties arose. Compare this information with the needs of the role.

If your company lacks MLOps expertise, involve a technical assessor who can examine pipelines and production deployment choices. Ask business stakeholders to assess the clarity of the explanations.

Record a conclusion for each criterion, including what has been demonstrated and what still needs further examination.