Lead Machine Learning Engineer

Description

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

Requirements

• Proficiency in programming languages such as Python, R, MATLAB, or C++.
• Familiarity with GraphDB, MongoDB, SQL/NoSQL, and other DBMS technologies.
• Expertise in relevant AI/ML applications, such as predictive maintenance, load forecasting, or optimization.
• Solid foundation in AI/ML techniques, including supervised, unsupervised, and reinforcement learning, deep learning, and large language models (LLMs).
• Experience with deep learning algorithms, reinforcement learning, NLP, and computer vision in applicable domains.
• Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Master’s or PhD in Computer Science, Information Technology, Electrical Engineering, or a related field.
• Understanding of system automation, protection, and diagnostics in relevant sectors.
• 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.
• Hands-on experience deploying ML models in production environments using MLOps principles.
• Experience with data modeling, containerization (Docker, Kubernetes), and distributed computing (Spark, Scala).
• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and microservices architecture.
• Excellent communication, organizational, and problem-solving skills, with a strong emphasis on teamwork, collaboration, and fostering inclusive environments.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance