Artificial Intelligence

• Play a hands-on, operational leadership role in advancing the organization’s applied artificial intelligence strategy across clinical, operational, and administrative domains
• Responsible for the day-to-day management of AI initiatives, supporting the full AI algorithm lifecycle—from intake and evaluation through deployment, monitoring, and optimization
• Work closely with clinical leaders, operational stakeholders, IT, Informatics, Legal, Risk, and external AI vendors to ensure AI tools are safe, effective, compliant, and aligned with organizational goals
• Ensure that AI delivers real, measurable value while maintaining trust, safety, and operational excellence

AI Manager

• Define and evolve enterprise AI Risk and Governance strategy aligned to business outcomes.
• Operationalize a federated governance model with centralized standards and decentralized execution.
• Establish clear decision rights for AI use cases, model deployment, and data usage.
• Own the AI Governance lifecycle including intake, risk tiering, validation, deployment, monitoring, and decommissioning.
• Ensure governance consistency across ML, GenAI, agents, and third-party tools.
• Establish data governance frameworks: ownership, classification, quality, lineage, and compliance.
• Partner with Cyber and Legal on privacy and regulatory adherence.
• Chair and/or participate in key governance forums (Council, Review Board, Architecture Committees) and ensure effective escalation.
• Define and enforce Responsible AI standards including bias, explainability, and control mechanisms.
• Develop and oversee model and agent registry, ensuring lineage, versioning, and performance tracking.
• Embed governance into platforms and pipelines; automate controls and monitoring.
• Define KPIs and ensure governance aligns to measurable business outcomes.
• Support and help drive adoption through enablement, training, and cultural change.

Director, AI Risk and Governance

• Translate business needs into technical systems solutions and architectural roadmaps
• Work on problems of diverse scope and complexity
• Expert in 24/7 operations with high performing systems
• Spearhead research, evaluation and deployment of AI and GenAI capabilities
• Bridge traditional IT Operations with emerging AI technologies
• Collaborate with Infrastructure and Cloud Operations teams to pilot and integrate AI/GenAI features
• Lead research and evaluation of AI and GenAI tools for Infrastructure Operations
• Develop and lead the strategic roadmap for AI adoption in Infrastructure Operations
• Identify automation opportunities and implement AI-enhanced runbooks

Infrastructure Operations Lead – Cloud and AI GenAI Enablement

• Own delivery of large and/or complex ML, DL, and Generative AI systems
• Design, implement, and evaluate AI systems for production
• Apply prescriptive analytics techniques to business problems
• Lead development and testing of ML algorithms
• Stay updated with advancements in AI/ML methods
• Benchmark against existing ML methods
• Develop prototypes and proof-of-concept implementations
• Lead consultation sessions with business partners
• Drive continuous improvement through novel techniques
• Identify actionable insights and communicate results

Senior AI/ML Scientist

• Develop and own the enterprise AI adoption strategy aligned with SupplyHouse's business objectives and long-term growth plans
• Identify high-impact AI use cases across procurement, operations, sales, customer service, and finance, and prioritize based on ROI and feasibility
• Serve as the primary internal authority on enterprise AI tools, platforms, and best practices including large language models, agentic workflows, and AI-powered analytics
• Monitor the AI technology landscape and advise executive leadership on emerging opportunities and risks
• Design and execute enterprise-wide change management programs grounded in proven methodologies to drive sustained, high-quality AI adoption across all levels and functions
• Act as a culture carrier for AI transformation championing a mindset of curiosity, continuous learning, and responsible experimentation throughout the organization
• Build internal champions, AI ambassadors, and center-of-excellence structures to scale AI literacy and usage across every business unit
• Develop training curriculum, playbooks, and role-based enablement programs to upskill employees on AI tools and workflows at every level, from frontline staff to senior leadership
• Create psychological safety around AI adoption by proactively addressing employee concerns about job displacement, skill gaps, and the pace of change positioning AI as a tool that augments human capability rather than replaces it
• Partner closely with the Human Resources team to embed AI competency into hiring profiles, onboarding programs, performance frameworks, and career development pathways
• Lead executive communications, all-hands presentations, and town halls related to AI strategy, adoption milestones, and organizational impact
• Identify and mitigate human-side risks of AI adoption including change fatigue, resistance patterns, and inequitable access to enablement resources across teams
• Partner with department heads across IT, Operations, Human Resources, Legal, and Finance to embed AI into core business processes
• Collaborate with the IT and Data Engineering teams to ensure secure, compliant, and scalable AI infrastructure
• Manage relationships with external AI vendors, consultants, and technology partners
• Coordinate with Legal and Compliance teams to establish responsible AI governance policies, data privacy standards, and risk frameworks
• Define KPIs and success metrics for AI adoption initiatives and report progress to the C-suite and Board as needed
• Establish feedback loops and continuous improvement processes to optimize AI tool performance and user experience
• Build business cases and ROI models to justify AI investments and scale successful pilots enterprise-wide
• Build, lead, and mentor a high-performing team of AI program managers, analysts, and change agents
• Foster a culture of experimentation, curiosity, and responsible innovation within the team and across the organization

Director, Enterprise AI Adoption

• Learn how teams across the business work (Customer Service, Operations, Merchandising, Marketing, Product, etc.) and identify high-impact opportunities where AI can help
• Partner with stakeholders to define problems clearly, map workflows, and estimate potential value and effort
• Research and evaluate AI tools and approaches (LLMs, copilots, workflow automation, search/RAG, lightweight agents) for business use cases
• Build and test small prototypes or proofs of concept (e.g.: prompt workflows, chat-based assistants, automation scripts, internal knowledge helpers)
• Create clear documentation and playbooks so others can reuse what you build (prompt guides, best practices, example workflows)
• Help run simple enablement activities like demos, office hours, training sessions, and collecting feedback
• Track results and learnings, including what worked, what did not, and recommendations for next steps
• Follow responsible AI practices, including data privacy, security, and quality standards

AI Enablement Intern

• Determines how decision support systems and AI-enabled solutions will provide the data required to make effective business decisions.
• Performs modeling of information sources, dataflows, and analytical pipelines.
• May coordinate the activities of a project team and monitor project schedules and costs for own projects.
• Create and maintain reporting and advanced analytical solutions leveraging enterprise data platforms.
• Ensure data quality, integrity, and alignment with enterprise data architecture.
• Develops and supports complex data warehouse-related and analytical applications, including scalable data pipelines and model ready datasets.
• The majority of time will be spent hands on coding, data analysis, and building reusable analytical/AI components.
• Provide training and guidance on analytics tools, models, and data products.

Advanced Analytics & AI Consultant

• Lead the design and execution of Compliance Monitoring and Oversight activities, including reviews, audits, and control assessments.
• Establish risk-based monitoring plans aligned to regulatory requirements and AI governance priorities.
• Oversee end-to-end engagement lifecycle: planning, testing, issue identification, and remediation tracking.
• Implement automated monitoring capabilities to enhance efficiency, scalability, and real-time risk detection.
• Leverage digital technologies and AI to enhance monitoring effectiveness and drive continuous program improvements.
• Synthesize findings into clear reporting, metrics, and actionable insights for stakeholders and leadership.
• Drive continuous improvement of monitoring methodologies, tools, and documentation standards.
• Provide advisory guidance on control effectiveness, regulatory alignment, and risk mitigation strategies.
• Facilitate stakeholder engagement, including interviews, issue resolution, and escalation management.
• Develop and maintain frameworks, job aids, and training to support consistent oversight execution.

AI Compliance Monitoring, Audit Advisor

• Serve as EnerSys’ executive leader and single point of accountability for enterprise AI strategy, execution, and outcomes.
• Translate enterprise and business strategies into a clear, multi‑year AI vision and roadmap aligned with growth, productivity, quality, safety, and risk objectives.
• Advise the CIO, ELT, and Board on AI trends, risks, opportunities, and competitive implications.
• Represent EnerSys externally with strategic AI partners, technology providers, and industry forums.
• Design, implement, and continuously evolve the enterprise AI operating model, including governance forums, funding and intake processes, prioritization methods, and decision rights.
• Chair and run the Executive AI Steering Committee, ensuring disciplined decision making, clear accountability, and execution follow through; set agendas, align key stakeholders, and communicate decisions and actions clearly.
• Establish enterprise standards for responsible AI, data usage, security, privacy, and risk management in partnership with Legal, Cybersecurity, IT Architecture, HR, and Compliance.
• Own AI investment governance, ensuring alignment with capital planning, transformation funding, and enterprise portfolio priorities.
• Own the global AI use case portfolio and prioritization framework, balancing short term value delivery with long term strategic capability building.
• Ensure AI initiatives progress efficiently from ideation to pilot to scaled production—eliminating “pilot only” stagnation.
• Define, track, and report enterprise AI value metrics, including operational efficiency, cost reduction, quality improvement, risk mitigation, revenue enablement, and customer impact.
• Regularly recalibrate the AI roadmap based on realized outcomes, business performance, and evolving priorities.
• Orchestrate cross-functional delivery across IT, data, digital platforms, business units, and external partners—creating shared plans, clear ownership, and fast decisions.
• Resolve enterprise level dependencies and escalations impacting delivery, adoption, or value realization by facilitating trade-off discussions, aligning decision makers, and driving timely, documented outcomes.
• Ensure AI initiatives are integrated with core enterprise platforms (ERP, data environment, digital products) rather than delivered as isolated solutions.
• Build scalable execution playbooks, delivery standards, and reusable components to accelerate adoption across regions and functions.
• Partner with HR and Communications to lead enterprise-wide change management, AI literacy, and workforce enablement.
• Sponsor AI capability building, including training programs, citizen developer models, communities of practice, and leadership education.
• Drive cultural adoption by positioning AI as a core business capability embedded in how work gets done—not as a technology experiment.
• Support workforce transformation planning, including role evolution, ethical considerations, and future skills development.
• Build, lead, and develop a high performing enterprise AI organization with clear roles, succession, and career paths.
• Set performance expectations, accountability, and development plans for direct and indirect reports.
• Foster a strong culture of accountability, collaboration, and execution discipline across the AI ecosystem.

VP Enterprise AI Enablement

• Design and implement agentic AI architectures, including multi-agent workflows, tool-calling systems, memory and state management, and orchestration logic for complex multi-step tasks.
• Develop and evaluate retrieval-augmented generation (RAG) pipelines, with attention to retrieval strategy, document chunking and indexing, embedding model selection, re-ranking, and end-to-end evaluation.
• Contribute to LLM evaluation and validation frameworks — defining test coverage, constructing evaluation datasets, assessing output reliability, and identifying failure modes through structured testing and adversarial analysis.
• Conduct prompt engineering and instruction design for domain-specific tasks, and support experimentation with parameter-efficient fine-tuning approaches where applicable.
• Perform model benchmarking and comparative analysis, including evaluation of commercial and open-source LLMs for specific task types, latency and cost tradeoffs, and domain adaptation requirements.
• Support integration of LLM and agentic components with broader system architectures, including data pipelines, APIs, and domain-specific tooling.
• Contribute to data preparation and preprocessing workflows for structured and unstructured industrial datasets, including cleaning, transformation, and schema design.
• Engage in the full engineering rigor expected of production AI/ML systems — including unit and integration testing, model verification and validation, experiment evaluation, simulation workflows, data and output visualization, and technical documentation and reporting — as continuous activities throughout the project lifecycle.

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