Head of AI Automation

Responsibilities

• Define and own the enterprise-wide AI production platform and engineering roadmap.
• Drive the progression of AI capabilities from validated pilots to full enterprise production on standard engineering patterns.
• Partner with operations and business sponsors across all Novelis regions and functions to translate automation requirements into deployable, governed, production-supportable AI systems.
• Set the multi-year technology direction for the enterprise AI platform, ensuring AI systems are engineered to scale reliably across the global manufacturing footprint.
• Lead the design, deployment, and operation of computer vision and edge AI systems for autonomous quality inspection, safety monitoring, scrap sorting optimization, real-time anomaly detection, and process automation at the factory edge.
• Build and operate predictive intelligence and control AI capabilities, including predictive models engineered to drive autonomous, closed-loop actions.
• Deliver enterprise knowledge management (RAG) solutions grounded in governed Novelis data, providing trusted retrieval-augmented assistants for operations, engineering, and corporate functions.
• Build and operate advanced agentic solutions on the enterprise AI platform, including multi-agent workflows and autonomous, production-grade systems with human-in-the-loop design, exception handling, and safe execution within business-defined rules.
• Build and operate enterprise MLOps infrastructure for model deployment, monitoring, drift detection and alerting, retraining execution, and production operations.
• Implement and operate the technical guardrails, explainability, logging, and controls required by the enterprise AI governance framework.
• Manage compute, model provider, and tooling costs, measuring, forecasting, and keeping expenditures within approved budgets.
• 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.
• Contribute to quarterly planning, feature scoping, and sprint execution aligned to the enterprise delivery roadmap and KPI framework.

Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field.
• 10+ years of experience in AI/ML engineering, data science, or AI systems delivery, with at least 5 years in a leadership role managing AI or engineering teams.
• Proven track record of delivering production-grade AI systems at enterprise scale, including experience transitioning AI pilots to full production deployment.
• Deep expertise across multiple AI domains such as computer vision, predictive and control modeling, MLOps, retrieval-augmented generation (RAG), or agentic systems.
• Hands-on experience with enterprise AI platforms and MLOps tooling (e.g., Azure AI Foundry) and production deployment patterns.
• Strong understanding of AI safety, governance, and risk management frameworks.
• Strong strategic thinking with the ability to define and execute multi-year AI platform roadmaps aligned to enterprise business objectives.
• Strong leadership, communication, and stakeholder management skills with the ability to influence at the executive level.