Position Overview
This role is for a forward-thinking, results-driven Head of AI Automation to lead the enterprise AI automation and production-AI engineering function within the Data, Analytics & AI organization. Based in Atlanta, GA, this role reports to the VP of Data, Analytics & AI and is accountable for designing, engineering, deploying, and operating Novelis’ production AI systems safely and reliably at scale—spanning computer vision and edge AI, predictive intelligence and control AI, enterprise knowledge (RAG) solutions, and advanced agentic systems—across Novelis’ global manufacturing and corporate operations.
This leader defines and owns the AI production platform and engineering roadmap and the enterprise MLOps backbone, and engineers AI systems to meet the safety, risk, and guardrail standards set by an independent AI governance function—progressing reliably from validated pilot to full enterprise production through its governance gates. The Head of AI Automation directs a multi-disciplinary global engineering organization across computer vision and edge AI, predictive intelligence and control AI, MLOps and AI production engineering, retrieval-augmented knowledge solutions, and advanced agentic systems built on enterprise AI platforms (e.g., Azure AI Foundry). This role requires strategic vision, deep AI/ML and production-engineering expertise, operational discipline, and enterprise-scale people leadership to deliver production-grade AI systems that drive measurable outcomes in yield, quality, cost, safety, and sustainability across 32 global facilities and four continents.
Capability Alignment
This role owns the following enterprise AI capabilities:
- Computer Vision & Edge AI
- Predictive Intelligence & Control AI
- MLOps & AI Production Engineering
- Knowledge Management Solutions (RAG Solutions)
- Advanced Agentic Solutions (AI Foundry)
Responsibilities
Strategic Leadership & AI Roadmap
- Define and own the enterprise-wide AI production platform and engineering roadmap—prioritizing delivery with operations and business sponsors, aligning to Novelis’ business strategy, 3×30 sustainability targets, and digital transformation goals, and holding the team to committed engineering dates.
- Drive the progression of AI capabilities from validated pilots to full enterprise production on standard engineering patterns, eliminating re-architecture at each deployment stage.
- 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 (e.g., Azure AI Foundry, MLOps tooling, and edge infrastructure), ensuring AI systems are engineered to scale reliably across the global manufacturing footprint without one-off re-architecture.
AI System Delivery & Technical Excellence
- 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—predictive models engineered to drive autonomous, closed-loop actions, including real-time process control, autonomous setpoint optimization, and machine actuation within engineered safety limits—in partnership with Operations.
- 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 (e.g., Azure AI Foundry)—multi-agent workflows and autonomous, production-grade systems with human-in-the-loop design, exception handling, and safe execution within business-defined rules.
- Partner with business and analytics teams to take democratized, citizen-built agentic solutions that require production-grade or autonomous execution and re-engineer them for safe, reliable production at scale.
MLOps & AI Production Engineering
- Build and operate enterprise MLOps infrastructure for model deployment, monitoring, drift detection and alerting, retraining execution, and production operations for all production AI models—operating to the thresholds, retraining approval criteria, and incident-response playbook defined by the AI governance function.
- Implement and operate the technical guardrails, explainability, logging, and controls required by the enterprise AI governance framework—providing model evidence and documentation for governance gates, audits, and approvals, and addressing remediation that the AI governance function directs across every deployed system.
- Set and publish AI platform operational SLAs covering system uptime, model-lifecycle cadences, and operational incident response times, and meet the guardrail-conformance standards defined by the AI governance function.
- Engineer reusable production patterns, reference architectures, and CI/CD for AI so capabilities deploy consistently across the global footprint and remain auditable on demand for governance review.
- Manage compute, model provider, and tooling costs—measuring, forecasting, and keeping expenditures within approved budgets.
Strategic Traceability & Enterprise Alignment
- 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.
People Leadership & Engagement
This role is a leader of people first and a manager of process second. The Head is accountable for intentionally engaging the team, specifically:
- Hold deliberately scheduled engagement sessions with employees at every level of the pillar — short, listening-first conversations, not status reviews — and maintain equivalent rhythm and access for remote and non-Atlanta team members. Open leadership meetings with people topics (growth, recognition, blockers) before task topics.
- Push decisions and ownership to the team. Sponsor ideas on their merit regardless of the originator’s tenure or title. Maintain a current growth plan for every team member and ensure each has a peer mentor, preferably from another pillar.
- Act with intention. Own the pillar’s engagement baseline and KPIs (retention, engagement-survey results, session cadence) and report them alongside delivery KPIs in monthly goal reporting. Apply the front-line engagement budget deliberately to build team connection.
- Build, lead, and scale a high-performing global engineering organization across computer vision engineers, predictive analytics and control AI engineers, MLOps and AI production engineers, RAG/knowledge engineers, agentic systems engineers, AI platform and reliability engineers, and AI product managers—organized into distinct sub-functions, each with its own technical leadership.
- Foster a culture of engineering excellence, innovation, responsible AI, and continuous improvement through mentoring, coaching, and professional development.
- Attract and retain top AI talent by creating a compelling vision, clear career growth paths, and a supportive team environment.
- Develop succession depth and technical leadership capacity across all AI disciplines.
- Own the function’s budget, AI compute and model-provider spend, and platform and tooling portfolio—accountable for cost-to-value across the production AI estate.
Success in this role is measured by an intentionally engaged, empowered team that delivers — not by delivery alone.
Cross-functional Collaboration & Enterprise Partnership
- Partner with operations, business sponsors, data and analytics teams, the AI governance function, and IT teams across all Novelis regions to translate strategic objectives into production-grade AI solutions.
- Serve as a trusted advisor to senior leadership on AI automation capabilities, delivery and operational posture, investment trade-offs, and emerging AI opportunities.
- Partner with the independent AI governance function throughout the lifecycle—submitting AI systems to intake and pre-deployment gates, providing model evidence and documentation, and addressing required remediation to meet enterprise governance standards.
Minimum 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.
Preferred Qualifications
- Master’s degree or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field.
- Experience in manufacturing, industrial, or sustainability-focused organizations operating across multiple global facilities.
- Experience deploying computer vision, edge AI, or predictive and closed-loop control systems in operational technology (OT) environments.
- Experience building enterprise RAG/knowledge solutions and production agentic systems on platforms such as Azure AI Foundry.
- Familiarity with AI regulatory frameworks, responsible AI practices, and enterprise AI policy development.
- Experience governing AI compute and model provider costs at enterprise scale.
Please note that we are unable to provide visa sponsorship for this position. Candidates must be legally authorized to work in the United States without the need for current or future sponsorship
What We Offer:
Novelis' benefits say a lot about how we care for each other. Our employees and their families have many different needs. As a result, our benefits offer choices on many levels and are high in quality, driven by the marketplace, and affordable. In addition to core benefits, we provide these unique to the industry benefits:
- Family Growth Programs: Paid parental Leave, Adoption Assistance, Fertility Treatment, Childcare Discount and Nursing Mom Support
- Employee Assistance Programs: free resources available 24/7 to you and your family in the areas of mental health, family life, and career and financial guidance
- Wellness Programs: incentives for wellness activities, wellness spending account, programs for building healthy habits, virtual physical therapy for joint, back, and pelvic health, health management programs and more.
- Diabetes Management Program
- Pet insurance
- Identity Theft Protection
- PerkSpot Discount Program
- Tuition assistance and career development programs