Artificial Intelligence

• Lead enterprise AI strategy and digital delivery for HR and workforce systems
• Oversee the implementation and optimization of people systems
• Champion the use of AI and advanced analytics to streamline workforce management
• Develop and execute a forward-looking AI-enabled workforce strategy
• Lead transformation initiatives that enable intelligent, data-driven, and digital-first operating models
• Deliver actionable insights into workforce trends, performance, and behaviors

Senior Director, AI, Workforce Innovation, Digital Delivery

• Drive global TA process standardization, governance, and operational rigor across all regions, ensuring consistency in how recruiting is accomplished while respecting regional nuances.
• Lead the AI strategy and adoption roadmap for Talent Acquisition — identifying, assessing, implementing, and measuring AI-enabled tools and capabilities (e.g., AI sourcing, interview scheduling, skills-based screening) that reduce cost-to-hire and improve quality and speed.
• Own TA programs including the Internal Job Opportunity Program (IJOP) and Employee Referral Bonus Program, managing policy governance, global alignment, and continuous improvement.
• Own the TA enablement framework, including recruiter onboarding, capability development, quality assurance, and the tools and platforms that equip recruiters to perform at a high level across all markets.
• Define, refine, and maintain the TA operational KPI framework — establishing the metrics that matter, building self-service analytics capabilities (e.g., Visier), and translating data into actionable insights and strategic recommendations for TA leadership.
• Coordinate cross-regionally on compliance-related infrastructure, process improvements, and technology rollouts to ensure the TA function operates with accountability, efficiency, and readiness for hiring at scale.

Director, Global TA Operations, Enablement, AI

• Set the multi-year strategy for ML, data, and BI
• Build and scale the Data Science / AI, Data Engineering, and BI Engineering organization
• Manage managers and tech leads
• Run performance management, career development, and retention
• Operate as a peer to VP-level leaders across various functions
• Own the architecture, deployment, and operation of the production ML

VP of AI, ML and Data Strategy

• Drive end-to-end delivery of production AI platforms
• Lead solution architecture, orchestrate model pipelines, and design multi-agent workflows
• Create reusable SDKs and frameworks
• Collaborate across product, engineering, security, and governance teams
• Ensure AI systems are scalable, observable, cost-efficient, and compliant
• Define design patterns and optimize operational pipelines
• Enable internal reuse of proven solutions
• Own the full lifecycle from architecting secure, compliant solutions to deploying and scaling them in production
• Work with Azure OpenAI, Azure AI Search, multi-agent orchestration, and API integrations for EHR, CRM, and member portals

Senior Artificial Intelligence Engineer

• Design, build, and maintain enterprise AI solutions and services that enable the scalable adoption of generative AI, machine learning, and agentic AI capabilities across the organization.
• Implement enterprise AI architecture patterns and technical standards defined by the Enterprise AI Architect, translating reference architectures and design patterns into production-ready solutions.
• Develop and support AI integration patterns that connect AI capabilities with enterprise applications, APIs, data platforms, and cloud services.
• Build and maintain AI-enabled services and components, including model interfaces, orchestration services, agent frameworks, and reusable AI tooling for enterprise teams.
• Support the implementation of Agentic AI workflows, including orchestration, tool integration, context management, and human-in-the-loop capabilities.
• Implement and maintain AI operational capabilities (MLOps/LLMOps) including model deployment pipelines, monitoring, observability, lifecycle management, and operational controls.
• Ensure AI solutions align with enterprise AI governance, responsible AI standards, and regulatory requirements, including data privacy, auditability, and model transparency.
• Support AI platform development and enablement, working with Platform Engineering and Data Engineering teams to deploy, scale, and maintain enterprise AI infrastructure and tooling.
• Other duties as assigned.

Engineer, Enterprise AI

• Design end-to-end AI systems for customer experience use cases.
• Architect reliable, production-ready AI solutions that go beyond prompt design, combining LLMs, deterministic workflows, tools, and orchestration layers.
• Define how AI interacts across the full journey (self-service, agent copilot, journey management, and back-office automation), including fallback strategies, human handoff, and failure handling.
• Optimize retrieval-augmented generation (RAG) and knowledge architectures to extend Genesys solutions where required.
• Design scalable knowledge and retrieval strategies that ground AI responses in enterprise data.
• Develop approaches for content structuring, chunking, embedding, and ranking to ensure accuracy, relevance, and freshness.
• Partner with customers to align AI outputs with trusted knowledge sources while balancing performance, latency, and governance requirements.
• Establish AI evaluation frameworks and quality measurement strategies - Define how success is measured for AI-driven experiences, including accuracy, containment, customer satisfaction, and business impact.
• Create test sets, evaluation methodologies, and feedback loops to continuously improve performance.
• Translate technical quality metrics into business-relevant outcomes to support customer decision-making and adoption.
• Engineer contextual AI experiences that leverage real-time data and conversation state - Design how AI systems incorporate dynamic context such as customer data, interaction history, and external signals.
• Optimize context management and prompt structure to maximize relevance while managing token limits and response quality.
• Ensure AI interactions remain coherent, personalized, and aligned across channels and touchpoints.
• Design for scalability, latency, and cost efficiency in enterprise environments - Evaluate and optimize AI solutions for real-world constraints, including response time (especially for voice), throughput, and cost at scale.
• Make informed tradeoffs across model selection, caching strategies, and architecture patterns to deliver performant and economically viable solutions.
• Ensure designs meet enterprise expectations for reliability and responsiveness.
• Work with Genesys Strategic Technology partners and GSI partners to build, refine, and optimize their Genesys Cloud development lab environments with a focus on AI.

Senior AI Partner Architect

• 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

• Own and execute the end-to-end AI strategy across Technology and Products, aligned to business priorities and delivered in close partnership with enterprise stakeholders—driving measurable improvements in productivity, quality, and cycle time.
• Lead AI-enabled and software engineering transformation across the full technology-to-product lifecycle, including agentic workflows, simulation-driven decision-making, predictive analytics, and closed-loop learning systems that accelerate execution and improve first-time-right outcomes.
• Drive and integrate cross-functional AI initiatives spanning: New DRAM, NAND, and Advanced Packaging Technology Silicon Design for Memory and Logic End-to-End Silicon Life Cycle Engineering DRAM Module and SSD Development Engineering Productivity Platforms and AI solutions.
• Architect, implement, and scale enterprise-grade Engineering AI platforms and engineering software solutions that support core workflows and mission-critical use cases, with strong foundations in security, reliability, and governance.
• Provide AI technology leadership and pathfinding, maintaining an external perspective on emerging AI innovations and translating advanced capabilities into practical, high-impact engineering solutions.
• Build and lead a robust external AI ecosystem, establishing strategic partnerships with leading AI companies, EDA vendors, academic institutions, and emerging startups.
• Establish clear value realization and accountability, defining success metrics, tracking benefits, and providing executive-level reporting for AI investments.
• Foster AI upskilling and an AI-driven culture across all global TPG functions, enabling broad adoption while ensuring responsible and secure use of AI technologies.
• Partner closely with the Micron AI Steering Committee and the Transformation Officer to shape and execute a coherent, company-wide AI strategy.

Vice President, AI for Technology and Products

• 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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