Artificial Intelligence

• 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

• Develop and maintain core School of Splunk Field Sales Enablement portfolio
• Design and deliver AI-focused onboarding and upskilling curricula
• Integrate critical technical sales skills into curriculum for learners to develop mastery
• Compose test questions, evaluations, and assessments that validate proficiency
• Develop and continuously refresh learning content that reflects the messaging and positioning related to Splunk platform
• Work with key stakeholders to develop technical seller training programs
• Serve as a trusted advisor to technical sales stakeholders and SMEs
• Update and optimize existing GFE training assets to reflect Cisco's current strategic direction

Curriculum Designer – AI Native

• Lead the analysis, design, and delivery of high-impact AI-enabled fraud use cases, partnering with internal teams, the AI CoE, and vendors to drive measurable outcomes across the fraud portfolio
• Supports AI-enabled fraud use cases and model-adjacent analytics through internal development efforts, AI CoE partnerships, and vendor solutions, contributing analytical execution, cross-team coordination, and delivery support
• Drive analytics-led problem framing across the full model lifecycle, including use-case sizing, feature ideation, population analysis, and data quality assessment to ensure solutions are well-scoped, analytically sound, and value-accretive
• Serve as a trusted advisor and point of coordination for AI and machine learning use cases within fraud, ensuring alignment with enterprise AI governance standards, regulatory expectations, and the AI Use Case Registry
• Support delivery rigor by coordinating milestones, dependencies, and priorities, proactively identifying risks and escalation needs across internal teams, AI CoE efforts, and vendor engagements
• Assess vendor solutions and performance through an analytical lens, evaluating effectiveness, overlap, and realized value relative to cost, risk-reduction objectives, and enterprise priorities
• Maintain strong cross-functional partnerships with Fraud Strategy, Operations, Digital, Risk, and Technology teams, contributing analytical insights to identify fraud gaps, emerging threats, and new use-case opportunities
• Translate complex analytical findings into clear, executive-ready narratives, articulating implications for fraud loss reduction, operational efficiency, and customer experience
• Provide consistent analytical updates and insights to senior leaders and governance forums, highlighting progress, risks, dependencies, and value delivered to support informed decision-making
• Support executive and ad hoc strategic fraud analytics requests, delivering high-quality, timely insights to inform prioritization, governance discussions, and enterprise strategy
• Evaluate modeling needs and constraints to inform sourcing recommendations, advising on when to pursue internal development versus AI CoE or vendor solutions based on risk, complexity, cost, expertise, and delivery timelines
• Provide hands-on analytical leadership to support internal fraud model development, including problem definition, feature strategy, data readiness, validation alignment, and pre-production preparation, while adhering to governance and control standards

Fraud AI Strategy & Enablement Analyst

• 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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