Senior Machine Learning Engineer,

pExcited to work for a key Belgian player in the energy landscape? The Belgian and European energy landscape is in full swing. Did you know that Elia Belgium plays a crucial role in this? As part of Elia Group, a European transmission system operator, Elia Belgium ensures the reliability and sustainability of the electricity supply in our country. Side‑by‑side with your talented colleagues, you will play a part in building a sustainable, green and reliable energy infrastructure that stimulates economic growth and reduces our ecological footprint. Only together can we make the biggest impact possible We believe in the power of diversity we are convinced that an inclusive and diverse work environment leads to more innovation, creativity and better results. /p h3Senior Machine Learning Engineer /h3 pRequisition ID 11562 /p pPosting Start Date 27/10/2026 /p h3Function /h3 pThe Senior Machine Learning engineer is the technical reference for the EL.
AI product, the Central IT AI platform that eases the lifecycle of Machine Learning and AI models — from build, to training, to deployment and monitoring. The role turns emerging AI capabilities into production‑grade, reusable platform features that accelerate the enablement, adoption and security of AI across Elia Group. Working hand in hand with the AI Architect and the Product Owner, the senior machine learning engineer owns the “how” technical feasibility, implementation patterns, operationalization and engineering quality of the platform. The role is deliberately hands‑on and enablement‑oriented. It is the execution counterpart of the AI Architect, who owns the target architecture, standards and governance framework. /p h3Responsibilities /h3 ul liShape the technical roadmap of EL.
AI by evaluating emerging AI capabilities and translating them into scalable, production‑ready platform features. /li liDrive the design, development and continuous evolution of the EL.
AI platform, with a strong focus on scalability, reliability, maintainability and operational excellence. /li liDevelop reusable frameworks, templates, pipelines and reference implementations that accelerate the adoption of AI solutions across the organization. /li liTranslate architectural principles into concrete implementation patterns and contribute to the continuous refinement of architectural decisions through operational and implementation feedback. /li liContribute to the evolution of AI governance by turning implementation experience into actionable standards, guidelines and best practices. /li liEnsure seamless integration of the platform within the broader AI, data and IT landscape of the Group, while maintaining close alignment with relevant stakeholder teams. /li liConduct technical evaluations, prototypes and proof‑of‑concepts to validate new technologies, assess feasibility and reduce implementation risks. /li liSafeguard technical consistency across the platform and actively contribute to reducing technical debt and improving the long‑term sustainability of solutions. /li liAct as a technical reference within the team by fostering engineering excellence and promoting best practices in software development, MLOps, CI/CD, testing, observability and documentation. /li liSupport product development and platform adoption through technical feasibility assessments, onboarding activities, operational guidance and the translation of relevant AI market developments into concrete recommendations for the product roadmap. /li /ul h3Your Profile /h3 ul liMaster’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent through relevant professional experience. /li liMinimum of 10 years of experience in AI, cloud, or data engineering, including at least 5 years delivering AI/ML solutions in production environments. /li liProven experience in designing, building, and operating platforms or shared technical capabilities used across multiple teams. /li liExperience as a technical reference, lead engineer, or technical lead within a multidisciplinary environment. /li liFluency in English; knowledge of Dutch, French and/or German is considered an asset. /li liStrong expertise in Machine Learning Engineering and end‑to‑end model lifecycle management, from development and training to deployment, monitoring, and continuous optimization. /li liSolid knowledge of MLOps, Generative AI, and Agentic AI, including LLM integrations, RAG architectures, agent frameworks, model governance, and automated operational processes. /li liExtensive experience within the Microsoft Azure AI ecosystem and cloud‑native engineering, complemented by knowledge of Azure Machine Learning, Azure AI Services, CI/CD, DevSecOps, and Infrastructure as Code. /li liExperience with modern data and AI platforms and platform engineering, including Databricks, MLflow, vector databases, Kubernetes, API integrations, and the design of reusable technical building blocks. /li liGood understanding of AI gove