Machine Learning Engineer

Responsibilities -

• You design, develop, and deploy robust machine learning models that address the unique challenges of power generation.
• These models are used to predict equipment failures, optimize maintenance schedules, enhance energy output, and more.
• You champion MLOps best practices to ensure the long-term sustainability and maintainability of data products.
• You implement robust monitoring, testing, and deployment pipelines.
• You work closely with domain experts, engineers, and operators to understand their needs, gather feedback, and deliver solutions that make a meaningful impact on their day-to-day work.
• You collaborate closely with the IT NEO Data Platform team, leveraging their shared Python packages and workflows to standardize the building and running of ML use cases.
• You stay abreast of the latest advancements in machine learning and explore emerging technologies, such as multi-agent reinforcement learning, to identify new opportunities for optimization.

Required Skills

• You have a natural understanding and strong interest in the energy domain, with a desire to delve into the complexities of electricity production and its unique challenges.
• You bring over 4 years of proven experience in industrializing machine learning products, from concept to production.
• You are fluent in English and Dutch/French, allowing for effective collaboration and communication in multilingual environments.
• You are proficient with AWS cloud services, including S3, Glue, Athena, and Sagemaker.
• You have demonstrated expertise in Python development, specifically for data science and machine learning.
• You possess broad knowledge of machine learning frameworks, such as Scikit-learn, XGBoost, PyOD, and Darts, enabling effective implementation of diverse ML solutions.
• You have strong DevOps experience, including Git, CI/CD pipelines, and Docker containers, ensuring smooth deployment and maintenance of data-driven solutions.
• Having experience building multi-agent Retrieval Augmented Generation (RAG) systems is a plus.
• Bringing demonstrated experience working in industrial environments, preferably within the utility sector is a benefit.
• Possessing expertise in time series anomaly detection techniques is a valued addition.
• Experience working with historians, such as Aveva OSIsoft PI is an asset.

Preferred Skills

• Experience working with historians, such as Aveva OSIsoft PI is an asset.