Senior Machine Learning Engineer,

Excited 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.

Senior Machine Learning Engineer

Requisition ID 11562

Posting Start Date 27/10/2026

Function

The 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.

Responsibilities

• Shape the technical roadmap of EL.AI by evaluating emerging AI capabilities and translating them into scalable, production‑ready platform features.
• Drive the design, development and continuous evolution of the EL.AI platform, with a strong focus on scalability, reliability, maintainability and operational excellence.
• Develop reusable frameworks, templates, pipelines and reference implementations that accelerate the adoption of AI solutions across the organization.
• Translate architectural principles into concrete implementation patterns and contribute to the continuous refinement of architectural decisions through operational and implementation feedback.
• Contribute to the evolution of AI governance by turning implementation experience into actionable standards, guidelines and best practices.
• Ensure seamless integration of the platform within the broader AI, data and IT landscape of the Group, while maintaining close alignment with relevant stakeholder teams.
• Conduct technical evaluations, prototypes and proof‑of‑concepts to validate new technologies, assess feasibility and reduce implementation risks.
• Safeguard technical consistency across the platform and actively contribute to reducing technical debt and improving the long‑term sustainability of solutions.
• Act 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.
• Support 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.

Your Profile

• Master’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent through relevant professional experience.
• Minimum of 10 years of experience in AI, cloud, or data engineering, including at least 5 years delivering AI/ML solutions in production environments.
• Proven experience in designing, building, and operating platforms or shared technical capabilities used across multiple teams.
• Experience as a technical reference, lead engineer, or technical lead within a multidisciplinary environment.
• Fluency in English; knowledge of Dutch, French and/or German is considered an asset.
• Strong expertise in Machine Learning Engineering and end‑to‑end model lifecycle management, from development and training to deployment, monitoring, and continuous optimization.
• Solid knowledge of MLOps, Generative AI, and Agentic AI, including LLM integrations, RAG architectures, agent frameworks, model governance, and automated operational processes.
• Extensive 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.
• Experience with modern data and AI platforms and platform engineering, including Databricks, MLflow, vector databases, Kuberne