Lead Machine Learning Engineer

Job Description Summary

We are seeking a Lead Machine Learning (ML) Engineer with solid experience typically gained over at least 5 years in large multinational manufacturing environments, ideally within the energy, smart infrastructure, or industrial automation sectors. The ideal candidate has a proven track record of independently leading and delivering ML projects in complex, data-intensive ecosystems.

In this position, you will be responsible for leading end-to-end ML initiatives, from problem framing and data preparation to model development, optimization, and deployment across edge and cloud platforms. You will independently drive ML project execution, ensuring technical excellence, scalability, and measurable business impact. You will collaborate closely with R&D, product teams, and other business units to support the development of innovative, reliable, and high-performance data-driven solutions.

Job Description

Essential Responsibilities:

• Lead the design, development, and deployment of scalable AI/ML models for grid innovation applications in the energy, smart infrastructure, or industrial automation sectors.
• Create innovative analytics to optimize grid system performance and product differentiation.
• Develop AI/ML applications for customer-driven use cases, including predictive maintenance and load forecasting.
• Validate and verify AI/ML proof-of-concepts in real-world environments, ensuring they meet the diverse needs of our customers.
• Monitor, maintain, and optimize deployed AI/ML models to continuously enhance their accuracy and performance.
• Manage the collection, structuring, and analysis of data to enable seamless AI/ML applications.
• Ensure that models are production-ready and continuously improve in line with emerging needs and technologies.
• Embrace MLOps principles to streamline the deployment and updating of ML models in production.
• Collaborate closely with cross-functional teams to identify business challenges and deliver AI-driven solutions that are efficient, equitable, and scalable.
• Integrate AI/ML solutions effortlessly into grid automation systems, whether in the cloud or at the edge.

• Experience typically gained over +5 years in large multinational companies within the energy sector or related industrial domains such as smart infrastructure or industrial automation.
• Master's or PhD in Computer Science, Information Technology, Electrical Engineering, or a related field.
• Solid foundation in AI/ML techniques, including supervised, unsupervised, and reinforcement learning, deep learning, and large language models (LLMs).
• Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Hands-on experience deploying ML models in production environments using MLOps principles.
• Expertise in relevant AI/ML applications, such as predictive maintenance, load forecasting, or optimization.
• Proficiency in programming languages such as Python, R, MATLAB, or C++.
• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and microservices architecture.

• Experience with data modeling, containerization (Docker, Kubernetes), and distributed computing (Spark, Scala).
• Familiarity with GraphDB, MongoDB, SQL/NoSQL, and other DBMS technologies.
• Understanding of system automation, protection, and diagnostics in relevant sectors.
• Experience with deep learning algorithms, reinforcement learning, NLP, and computer vision in applicable domains.
• Excellent communication, organizational, and problem-solving skills, with a strong emphasis on teamwork, collaboration, and fostering inclusive environments.