AI Product Lead

🏢 Novelis

This role is responsible for leading the product vision, roadmap, prioritization, and lifecycle management of AI solutions across Novelis' global manufacturing operations, with a primary focus on visual intelligence, computer vision, machine learning, and predictive analytics use cases. Reporting to the Head of AI Automation & Enablement, this role owns the end-to-end product flow—from use case discovery, feasibility assessment, prioritization, planning, backlog management, release definition, deployment, monitoring, adoption, and continuous improvement. This is a hands-on leadership role that combines product ownership with direct people management, leading a team of developers responsible for building and sustaining AI solutions. The AI Product Lead partners closely with plant operations, quality, safety, maintenance, data engineering, MLOps, and software engineering teams to translate manufacturing challenges into practical, scalable AI products. Because this role sets technical direction and supports the growth of an engineering team, hands-on experience building AI, ML, computer vision, or software solutions is strongly preferred. Principais atividades: - Define and maintain the product roadmap for assigned AI capability domains, prioritizing use cases with operations and business sponsors and ensuring alignment with Novelis' enterprise AI strategy, manufacturing priorities, and measurable business value. - Conduct use case discovery, feasibility assessment, and business value analysis to identify and prioritize the highest-impact AI automation opportunities across the global manufacturing footprint. - Translate manufacturing and operational challenges into clear product requirements, acceptance criteria, and release plans, balancing delivery speed with quality, governance, and long-term supportability. - Own the product backlog and full lifecycle from concept through production deployment, monitoring, and continuous improvement, providing visibility into progress, risks, dependencies, and trade-offs. - Partner with plant operations, quality, safety, maintenance, and business stakeholders to translate operational pain points into deployable, governed AI solutions that drive measurable improvements. - Communicate product vision, roadmap progress, delivery timelines, and trade-offs to senior leadership and cross-functional partners. - Coordinate with Data Engineering, Master Data Management, and Data Quality teams to align data dependencies, standards, ownership, and readiness, and with MLOps for production deployment and monitoring requirements. - Ensure all AI products comply with the enterprise AI safety and governance framework and are engineered to scale reliably across multiple plant environments. - Align work execution to Novelis' enterprise strategic data outcomes including trusted data, operational reliability, metal flow optimization, 3×30 sustainability goals, and cash focus/operational efficiency. - Support the enterprise Data & AI Governance framework, ensuring governance is embedded into all workflows and deliverables. - Lead, manage, coach, and develop a team of developers, including goal setting, regular one-on-ones, performance feedback, career development, and workload balancing across projects. - Provide clear priorities, technical direction, and decision-making support while giving engineers ownership over solution design, implementation choices, and technical quality. - Maintain team focus on product outcomes, adoption, value realization, and responsible delivery, balancing speed, quality, governance, and long-term supportability. Requisitos: - Bachelors degree in Computer Science, Data Science, Engineering, or a related field. - Experience across AI/ML product management, data science, software engineering, computer vision, predictive analytics, or AI systems delivery. - Demonstrated experience leading AI product or solution lifecycles from discovery and planning through production deployment, adoption, monitoring, and continuous improvement. - Experience leading technical teams, including direct people management or formal team leadership of developers, data scientists, or engineers. - Experience applying agile product management practices, such as backlog management, prioritization, release planning, acceptance criteria definition, and progress visibility; formal Scrum Master experience is helpful but not required. - Working knowledge of machine learning, computer vision, predictive modeling, software development, MLOps, or related AI technologies, with enough technical depth to challenge assumptions and guide delivery trade-offs. - Strong communication and stakeholder management skills, with the ability to translate technical concepts into business language and connect business needs, feasibility, delivery capacity, and value into clear product decisions. Informações adicionais: - IT owns the AI systems, their engineering, operation, and guardrails; operations and bu…