Machine Learning Engineer

ph3Job Description – Machine Learning Engineer /h3 h3Mission Context /h3 pThe Machine Learning Engineer plays a key role in enabling the bindustrialization of Machine Learning and AI solutions /b within the enterprise. The mission of the role is to promote and apply bbest practices in production‑ready ML development /b, ensuring that AI solutions are robust, scalable, monitored, and fully integrated into IT production environments. /p pMachine Learning Engineers bridge the gap between bAI Analytics teams and IT production /b, ensuring that Machine Learning models deployed to production are supported by appropriate data pipelines, infrastructure, automation, and monitoring from both a technical and business perspective. /p pThey contribute to the full lifecycle of AI services, from design and development to deployment, monitoring, and continuous improvement. /p h3Key Responsibilities /h3 pMachine Learning Engineers contribute to Machine Learning projects by: /p ul liCollaborating closely with bData Scientists /b to define and develop solutions that meet business requirements while taking bproduction constraints /b into account (e.g. performance, scalability, latency, data volumes). /li liSupporting the selection of appropriate binfrastructure and serving models /b, including data ingestion patterns, synchronization models, and API designs (real‑time vs batch processing). /li liContributing to the bautomation and industrialization of ML pipelines /b, including: /li liContainerization and image creation (Docker / VMs) /li liPreparation of unit, regression, and integration tests /li liCI/CD integration for ML components /li liSupporting Data Scientists in the use of bexisting industrial platforms and CI/CD tools /b to build, deploy, and monitor AI services. /li liWorking closely with bIT Production teams /b to support the configuration and parameterization of target environments. /li liEnsuring models in production: /li liRun reliably and without errors /li liAre retrained when required (including automated retraining where applicable) /li liAre monitored from both bIT (technical) /b and bbusiness (performance, quality) /b perspectives. /li /ul h3Agile Delivery Context /h3 pThe Machine Learning Engineer typically works in bAgile delivery environments /b, contributing within cross‑functional teams that combine analytics, engineering, and testing expertise. The role requires close collaboration, continuous feedback, and a strong delivery mindset focused on stable and reusable solutions. /p h3Required Experience Knowledge /h3 h3Experience /h3 ul libMinimum 4 years of relevant experience /b as a Machine Learning Engineer, ML Platform Engineer, or similar role /li /ul h3Technical Skills /h3 h3Mandatory /h3 ul liStrong experience with bcontainerization and virtualization /b (Docker, VMs) /li liExperience with bAI platforms and development environments /b /li liCI/CD pipelines, preferably bGitLab CI /b /li liCode, data, and model versioning practices /li liAdvanced bPython /b development /li liPackage management and dependency management /li libPostgreSQL /b /li /ul h3Preferred /h3 ul liExperience integrating systems across different technologies (distributed systems, mainframe environments) /li liModel optimization and compression techniques /liliELT / ETL tools /li liBig data technologies (e.g. bApache Spark /b) /li liData flow processing frameworks /li liData visualization tools /li /ul h3Business Methodology /h3 h3Mandatory /h3 ul liPractical knowledge of bAgile methodologies /b /li /ul h3Language Requirements /h3 ul libEnglish: mandatory /b /li libDutch: nice to have /b /li libFrench: nice to have /b /li /ul h3Working Model /h3 ul lib50% on‑site / 50% remote /b working arrangement /li /ul h3Soft Skills /h3 ul liStrong communication skills (verbal and written) /li liResults‑driven with a strong sense of ownership /li liHigh attention to detail and rigor /li liCreative and analytical problem‑solving mindset /li liProactive in continuous learning and knowledge sharing /li liAwareness of efficiency and quality of delivery /li liAbility to think beyond existing processes and frameworks /li liPositive, energetic, and collaborative team player /li liOpen to change, feedback, and diverse perspectives /li /ul /p