AI Technical Product Lead

About the role

Publicis Sapient is looking for an Associate Director-level AI Product Manager to join our Product Management craft, focused on renewable energy and solar. This role sits at the intersection of clean-energy business strategy, artificial intelligence, data, and engineering. The AI Product Manager will identify and prioritize high-value AI opportunities across the solar and renewable energy value chain, translate business and user needs into a clear product vision and roadmap, and lead multidisciplinary teams from discovery and experimentation through scaled adoption. The ability to speak credibly with renewable energy leaders, data scientists, engineers, operators, and client stakeholders is essential.

Responsibilities

• Partner with solar and renewable energy clients, product leaders, operators, data scientists, machine learning engineers, software engineers, architects, designers, and strategists to identify business problems that can be solved responsibly with AI.
• Own the AI product vision, strategy, roadmap, and prioritized backlog for solutions supporting areas such as solar asset performance, generation forecasting, predictive maintenance, anomaly detection, energy trading and optimization, customer acquisition, project development, field operations, and grid integration.
• Lead discovery across utility-scale, commercial and industrial, and distributed solar workflows, converting customer and operational needs into product hypotheses, epics, features, user stories, acceptance criteria, data requirements, model requirements, and non-functional requirements.
• Evaluate where machine learning, generative AI, computer vision, forecasting, optimization, and intelligent automation can create measurable value, while distinguishing true AI opportunities from problems better solved through workflow, analytics, or platform improvements.
• Collaborate directly with data science and engineering teams to define model objectives, training and inference data needs, evaluation metrics, human-in-the-loop workflows, integrations, APIs, cloud architecture, monitoring, and production-readiness requirements.
• Drive AI product experimentation from problem framing and prototype development through pilot, validation, deployment, adoption, and continuous improvement.
• Translate complex AI concepts, model outputs, uncertainty, limitations, and technical trade-offs into clear business decisions