AI & ML Engineer - Automotive Application

Engibex; Sint-Truiden; On-site; EUR 90,000 - 130,000; Full time

Job summary

ENGIBEX is seeking an AI engineer to join a Limburg-based automotive R&D project. You will own AI/ML delivery end-to-end, from scoping with engineers to deployment and documentation, using Python in production and collaborating with an existing AI engineer.

The role emphasizes on-site work four days a week, with a strong focus on anomaly detection, internal LLM tooling, and new AI capabilities for engineering teams.

Qualifications

• Hands-on AI/ML delivery experience is required.
• Production-level Python in real projects is essential.
• Ability to scope, build, deploy and maintain software from idea to running solution.
• Fluent professional English.
• On-site work in Limburg four days a week.

Responsibilities

• Own topics end-to-end, in collaboration with the client and ENGIBEX peers.
• Report to the client’s engineering lead and engage ENGIBEX management with regular reviews.
• Scope problems with engineering teams, build the solution, deploy it, and document it.
• Drive adoption of AI tooling across engineering groups and ensure maintainability.

Skills

AI/ML delivery

Python production

End-to-end development

English fluency

On-site in Limburg

Tools

Vector CANape

MF4

Slurm

Azure

Docker

Linux

Job description

ENGIBEX is a Belgian R&D engineering consultancy, founded fourteen years ago, with around 95 people. Most of us are engineers with a master, PhD or postdoc background, working on our clients' R&D projects in powertrain electrification, embedded software, mechanical design, simulation, test and validation.

We work in two modes. Most of our engineers work on site within a client team. In a smaller number of cases, including this one, we take a work package: ENGIBEX owns a topic end to end and is accountable for the result, not just for the headcount.

Whatever the mode, ENGIBEX is your employer. Your contract, your salary, your car and your career path sit with us, and they do not end with a project.

Le poste :

The project

Our client is an automotive R&D centre in Limburg, Belgium, specialised in hybrid transmissions. They design, develop and validate their own products, with their own test facilities.

They are not a software company, and that is precisely the point. Over the past years, AI has moved from a side experiment to something their engineering teams actively ask for: a way to read their test data faster, to find their own documentation, to compare one programme against the history of all the others. The requests now come in faster than one person can absorb.

ENGIBEX runs that AI portfolio as a long-term work package. We scope the topics with the client, build the tools, deploy them and maintain them.

The team and how we work

The first ENGIBEX AI engineer has been in place since mid 2026 and owns the existing tools. You will be the second.

The client's R&D organisation is built on pairs: two people per discipline, so that topics can be shared, covered during absences and swapped when interests change. This role creates the second seat on the AI side.

Concretely, that means:

• You and your colleague split the portfolio between you, based on interest and availability, and you can rotate topics over time.
• You report technically to the client's virtual engineering leader, on a daily basis.
• Your ENGIBEX business manager follows the project closely, with regular touch points, and formal project reviews with you and the client several times a year.
• You are not alone on an island: you have a peer on the same topics, an ENGIBEX technical network behind you, and the possibility to bring in additional ENGIBEX support when a topic needs it.

What you will work on

The scope grows with the demand. Today it covers three families of topics.

Anomaly detection on test-bench data

Transmissions are run through accelerated durability tests on dedicated test benches, instrumented with a large number of sensors. The goal is to detect a developing failure from the signals, early, while the test is still running.

A model already exists and works offline on historical data. The current challenge is the step that nobody in the market seems to have taken on this kind of application: moving it online, so that it monitors a running test in real time, surfaces anomalies on a dashboard, and eventually raises alerts, without ever interfering with the test itself.

That involves retraining on new transmission families, separating real anomalies from legitimate operational events, proving the accuracy to the test engineers who will rely on it, and then scaling it from one test bench to many.

Internal LLM assistant

The client runs an internal, retrieval-based assistant on their own technical documentation, used by several hundred engineers. It has no access to the internet and is fed entirely by internal documents.

The work here is a mix of product and engineering: retrieval quality, document indexing, access management and permissions, new use cases requested by teams, deployment hygiene, and the unglamorous but decisive part, getting more people to actually use it.

New AI tooling for the engineering teams

This is the part that keeps growing, and where you will have the most freedom. Examples currently on the table:

• Comparing the requirements of a new programme against the history of past programmes, inside their requirements management platform, so that engineers stop rediscovering what was already decided.
• Predicting physical quantities in real time, such as component temperatures, from test or simulation data, to support calibration work.
• Applications around their simulation activities.

You will not be handed a backlog of tickets. You scope the problem with the engineering teams, build the solution, deploy it, document it, and convince people to use it.

Technical environment

• Language: Python, for everything.
• Data: sensor time series acquired with Vector CANape, stored in MF4, read in Python.
• Compute: an internal simulation cluster on Red Hat Linux with Slurm, plus a GPU workstation. Windows workstation with WSL for day to day work.
• Cloud: Azure for the LLM backbone.
• Reality check: the estate was built incrementally by different people over several years. Part of the job is to bring it to a proper engineering standard: a real development workflow, clear documentation, and deployments that do not go straight to production. If that prospect bores you, this is not your role. If it is the kind of thing you enjoy fixing, you will have a free hand.

Profil recherché :

Essential

• Hands-on AI/ML delivery, not AI as a side interest. At least one of: anomaly detection or time-series machine learning, LLM and RAG applications, or applied machine learning on industrial or scientific data.
• Python at production level, not notebook only.
• Able to take a tool from idea to running software on your own: scoping with stakeholders, building, deploying, documenting, maintaining.
• Fluent professional English. It is the working language of the team.
• Able to work on site in Limburg four days a week. The site is roughly one hour from Brussels and well connected to Leuven and Hasselt.

Valued, not required

• Automotive, powertrain or transmission background. Our client values it highly for the long run, because it makes it far easier to move you to another topic once a tool is finished. A strong AI profile from another industrial or scientific field is very welcome.
• Experience with measurement or test-bench data: Vector CANape, MF4, sensor time series.
• Linux, HPC or Slurm clusters, Azure, Docker.
• Requirements management tools such as Polarion, Codebeamer or DOORS.
• Having actually deployed a model in production, and not only offline, will set you apart immediately.
• An interest in connecting the project with academic research, for example through university collaborations or European R&D projects, is a bonus we can act on.

There is no fixed seniority threshold. What matters is what you have actually delivered.

Why this role

• Real ownership. You are not one data scientist among thirty. You own your topics, and your work is visible across the whole R&D organisation.
• Variety. Time series, LLMs, tooling, deployment. The portfolio keeps moving, and so does your scope.
• Visible impact. These tools are used by engineers every day, and a successful online anomaly detection deployment would be a first in their group.
• Long term. This is a lasting partnership between ENGIBEX and the client, not a short assignment.
• A peer to work with. You join an existing AI engineer, not an empty seat.