Lead Engineer - Machine Learning Applications

Essential Responsibilities:

• Lead the design, development, and deployment of scalable, high-performant, maintainable, and reliable ML and generative AI models for grid innovation applications within Grid Automation.
• Develop AI/ML applications for customer-driven use cases, including predictive maintenance, anomaly detection, failure analysis, optimized control and forecasting.
• Monitor, maintain, and optimize deployed AI/ML models to continuously enhance their accuracy and performance.
• Establish clear validation frameworks to ensure models meet required performance standards and business objectives.
• Design and conduct experiments to test and validate AI/ML models in the context of energy systems and grid automation applications (model selection, design, tuning, testing, refining, validation, optimization and deployment).
• Identify and address discrepancies between expected and actual model behavior, providing actionable insights to improve model performance.
• Establish test procedures to validate models with real and simulated grid data.
• Support the design, building and maintenance of MLOps pipelines in collaboration with team Architects, MLOps Engineers and other partners.
• Embrace MLOps principles to streamline the deployment and updating of ML models in production.
• Integrate AI/ML solutions effortlessly into grid automation systems, whether in the cloud or at the edge.
• Ensure that models are production-ready and continuously improve/evolve in line with emerging needs and technologies.
• Manage the collection, structuring, and analysis of data to enable seamless AI/ML applications.
• Ensure data adheres to data governance policies and industry standards.
• Collaborate closely with cross-functional teams to identify business challenges and deliver AI-driven solutions that are efficient, accurate, reliable, maintainable and scalable.
• Communicate validation results, insights, and recommendations clearly to stakeholders, including product managers and leadership teams.
• Build necessary understanding and expertise overtime to design and develop product features and applications such as Protection, Control, Monitoring and communication along with the AI/ML applications on the product

Must-Have Requirements

• Masters or PhD in Data Science, Computer Science, Information Technology, Electrical Engineering, or a related field with hands-on experience as ML Engineer.
• Proven ML Engineer experience in the energy, smart infrastructure, or industrial automation sectors, with expertise in system protection, automation, monitoring, and diagnostics, typically acquired through a minimum of 5 years of service.

Solid experience developing and validating AI/ML models, ensuring they meet business and technical requirements.

• Excellent foundation in AI/ML and statistical techniques, including supervised and unsupervised learning.
• Experience with deep learning algorithms, reinforcement learning, NLP, large language models (LLMs), small language models (SLMs) and computer vision.
• Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Proficiency in programming languages such as Python, R, MATLAB, C# or C++.
• Hands-on, demonstrable experience deploying ML models in production environments using MLOps principles.
• Experience with time-series analysis, signal processing, load forecasting, optimization and predictive maintenance relevant to energy systems and grid operations.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and deployment of models in cloud environments.
• Familiarity with GraphDB, MongoDB, SQL/NoSQL, and other DBMS technologies.
• Strong knowledge of statistical techniques, model technologies, performance metrics, and validation methodologies for AI/ML models.
• Experience with data visualization tools such as Tableau, Power BI, or similar to effectively present validation results and insights.
• Excellent communication, organizational, documentation and problem-solving skills.

Nice-to-Have Requirements:

• Familiarity with big data tools and technologies, such as Hadoop, Kafka, and Spark.
• Familiarity with data governance frameworks and validation standards in the energy sector.
• Experience with containerization (Docker, Kubernetes), and distributed computing (Spark, Scala).
• Understanding of system automation, protection, and diagnostics for power utilities and industrial customers.