> Markdown version of [/jobs/ext/12342-ai-ml-engineer](https://www.wearedevelopers.com/jobs/ext/12342-ai-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** Sitemark - **Location:** Leuven, Belgium - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Vision, Backup Devices, Python (Programming Language), PostgreSQL, Object Detection, TypeScript, Management of Software Versions, ReactJS, Delivery Pipeline, Deep Learning, Machine Learning Operations - **Published:** May 31, 2026 - **Apply:** https://be.indeed.com/viewjob?jk=175613f8227d3a14 ## About the Role Do you have experience in TypeScript?, Must-have * Strong applied computer vision / deep learning experience. You've trained, fine-tuned, and debugged CV models - not just consumed APIs. You understand what's happening inside the models you use. * Hands-on with the experimental loop: dataset curation, augmentation, training, error analysis, iteration. You're comfortable when results are bad and know how to diagnose why. * Pragmatic, product-oriented mindset. You can reason about how a model will be used in practice and what "good enough" looks like for the business. You prefer the shortest path to a real result. * Strong fundamentals and clean engineering instincts. You write code meant to live in production - readable, testable, maintainable - not just notebook scratch. * Open to learning the integration side. You don't need to be a senior full-stack engineer on day one, but you should be motivated to grow into MLOps and integration work, and comfortable touching code beyond the model itself. * High intelligence and learning velocity. We care more about how you think and how fast you grow than about years on a CV. * Comfortable working in English in a small, fast-moving team. Big plus * Experience with aerial / drone / remote-sensing imagery (orthomosaics, geo-referencing, multi-band, large images). * Non-visual imagery (thermal, multispectral) experience. * Detection, segmentation, keypoint, or multi-scale architectures applied to large or high-resolution images. * MLOps experience in production: experiment tracking, reproducible training, model registries, monitoring. * Full-stack experience (Python, TypeScript, React, Postgres) - you'll get plenty of opportunities to use it. * Weakly- or self-supervised learning, active learning loops. ## Description You'll own the AI/ML side of our platform: training and improving the computer-vision models that power our products, and making sure they actually ship and perform in production. Your work will raise our throughput across model implementation, training runs, and dataset iteration - directly unblocking the team and our customers. We're looking for a pragmatic engineer-scientist who delivers computer-vision solutions and knows how to navigate the landscape. Models exist to solve real problems - if an off-the-shelf model fine-tuned on our data does the job, that's a great answer. We care about results in the product, not novelty in a paper. No solar or energy background required - we'll teach you the domain; curiosity matters more. Requirements What you'll do * Level up the MLOps backbone that lets us ship models reliably: experiment tracking, reproducible training, dataset versioning, model registry, deployment pipelines, monitoring in production, and a feedback loop from labeled operations data back into training. This is where AI work meets engineering, and it's a big part of what makes this role impactful. * Train, fine-tune, and ship computer-vision models for tasks like thermal anomaly detection and classification, defect detection on high-resolution imagery, object detection on drone imagery, and stitching/co-registration support. * Run the full experimental loop: curate and improve datasets, design training runs, analyse errors, iterate. * Tackle harder architectural problems when they matter - for example, models that need to reason over large spatial context (entire sites, not just tiles) where a standard fixed-resolution detector falls short. * Integrate models into the product end-to-end. Your model isn't done when the metric looks good - it's done when it's running on real data in the platform and making the team or the customer faster. * Reason about business impact. Pick problems and approaches based on what actually moves the needle for our products and operations., * You report to the Head of Product & Engineering. Coaching and technical sparring with the Engineering Lead. * You'll work in cross-functional squads with platform engineers and our product team. * You'll partner closely with the operational teams and our customers. Tight feedback loop. * We value shipping over perfection, and getting the architecture right when it matters. Why this role is interesting * Real impact, fast. We have a clearly identified gap, a concrete roadmap, and customers waiting on the results. Your models will ship. * Breadth. From dataset and model work, through MLOps, into product integration. You'll grow across the stack as much as you want to. * Strategic seat. AI is central to where Sitemark is going. You'll help shape that direction, not just execute on it. * Pragmatic culture. We care about results, not theatre. 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