Artificial Intelligence

• Serve as a main technical voice for your account team
• Build deep, trusted relationships with C-suite and technical stakeholders
• Demonstrate how Salesforce's capabilities complement OpenAI's models
• Take ambiguous business problems and apply AI-first mindset
• Guide customers through solution evaluation and analysis
• Conduct deep-dive discovery with engineering, IT, and business teams
• Design and deliver compelling product demonstrations
• Orchestrate cross-functional solution teams
• Maintain understanding of the current AI competitive landscape
• Act as a feedback loop between OpenAI and Salesforce’s Product organization

Lead Account SE, Strategic 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

• Drive Lean transformation across the full enterprise value stream
• Coordinate and manage AI-enabled continuous improvement initiatives
• Partner with Operations, Finance, HR, Accounting, Procurement, and Technology teams to eliminate waste and accelerate throughput
• Design, facilitate, and execute workshops to identify and prioritize improvement opportunities
• Establish and track KPIs and performance metrics for Lean initiatives
• Serve as the AI Coordinator for the Process Excellence function
• Manage concurrent process improvement and AI-enabled projects
• Build and maintain strong relationships across different departments
• Develop training materials and standard work documentation

Senior Process Excellence Engineer – AI Coordinator

• Architect internal productivity revolution and analyze workflows.
• Rapidly prototype and refine agentic solutions.
• Help build and deploy production grade AI agents.
• Integrate AI agents with enterprise resources.
• Define and track productivity & ROI.
• Stay at the forefront of AI developments in the media industry.

Director, Applied AI – Strategy

• Lead Development of the Enterprise AI Transformation Agenda
• Partner with executive leadership to shape and maintain a multi-year enterprise AI transformation roadmap aligned to strategy and financial objectives
• Translate strategic AI priorities into a sequenced
• executable portfolio of initiatives across business units and functions
• Ensure AI initiatives are incorporated into annual planning
• investment governance
• and long-range financial plans
• Lead execution of AI initiatives
• ensuring accountability
• delivery discipline
• and transparency
• Establish and run AI TMO governance forums
• executive readouts
• and performance tracking
• Identify risks
• dependencies
• and constraints; proactively drive mitigation and decision-making
• Partner with business leaders to define
• track
• and realize financial and operational value from AI initiatives
• Ensure AI solutions are operationalized into workflows
• decision processes
• and operating models
• Scale successful pilots into repeatable enterprise capabilities
• Design and evolve the enterprise AI operating model
• including roles
• responsibilities
• and ways of working
• Support enterprise enablement efforts to build AI literacy and adoption
• Act as a senior change leader ensuring alignment between leadership intent and organizational behavior
• Serve as a trusted partner to the Executive Leadership Team (ELT) on AI priorities
• Provide hands-on leadership for mission-critical AI and transformation initiatives
• Prepare and deliver executive-level communications
• insights
• and recommendations.

Director – AI Transformation Management

• Builds and/or executes business initiatives and/or processes, while partnering with stakeholders and partners.
• Solves complex problems and delivers on business initiatives and processes by applying subject matter expertise and technical knowledge of the business.
• Engages with key stakeholders, partners, and business leadership to deliver on business objectives.
• Performs analytics, assessments, and/or interpretation of data and/or information to identify business opportunities, derive insights, and measure outcomes.
• Develops strategic and tactical plans to drive progress towards business goals and objectives.
• Define, develop, and execute enterprise‑level initiatives that advance Client Servicing priorities, with a focus on AI‑enabled efficiency, operational performance, and measurable business outcomes.
• Lead the identification, coordination, and delivery of AI‑driven efficiencies across Client Services, ensuring initiatives are aligned, resourced, and governed effectively.
• Oversee multiple tactical deployments occurring simultaneously (AI, process optimization, workforce planning, etc.), ensuring prioritized sequencing, dependency management, and consistent execution discipline.
• Establish and enforce governance standards specific to AI‑related data usage within Client Services. Ensure controls, data quality, model inputs/outputs, reporting mechanisms, and risk management practices meet enterprise expectations.
• Maintain initiative scorecards, assess risks, track milestones, and drive corrective actions to protect delivery timelines and business outcomes.

Business Strategy & Initiative Manager – AI Horizontal Efficiencies

• 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

• Produce social content across the content-driven platforms (YouTube and IG to start), with a clear understanding of what performs where and why.
• Audit and reimagine social and communications workflows through the lens of GEO — ensuring content is structured, distributed, and formatted to be discovered and cited by AI-driven search and answer engines.
• Build and manage a social content pipeline leveraging AI-native tools when appropriate: from ideation and scripting through production, editing, scheduling, and performance analysis.
• Evaluate, pilot, and implement AI tooling across the full content lifecycle — not just for social, but across communications, content marketing, and editorial functions.
• Build durable systems — playbooks, templates, governance frameworks, and training — so the team can produce at scale without the quality or consistency slipping.
• Serve as the team's internal authority on the AI landscape: what's production-ready, what's coming, and what requires us to change how we work now.

Head of Communications – AI Systems, Social Production

• 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.

Applied AI – Graduate Intern

• Serve as the primary analytics leader across eCommerce, Marketing, Finance, Retail, Merchandising, Inventory Planning, and Operations.
• Own problem definition for analytics - determining which business questions matter and what decisions insights should inform.
• Move analysis beyond “what happened” to “why it happened,” “how it happened,” and “what we should do next.”
• Proactively mine data to identify risks, opportunities, inefficiencies, and performance drivers.
• Influence senior leaders through clear implications and actionable recommendations.
• Own the integrity, governance, and usability of core commerce, customer, and operational datasets across platforms (eCommerce, POS, CDP, email, data warehouse).
• Validate insights using business context and commercial logic — not just technical correctness.
• Define, document, and continuously evolve company-wide KPI frameworks in partnership with Finance and functional leaders.
• Identify and resolve cross-functional metric misalignment to ensure a single, trusted understanding of performance.
• Establish standards for data quality, documentation, and analytical transparency across the organization.
• Leverage AI as a core component of advanced analytical work, including hypothesis generation, exploratory analysis, pattern detection, insight synthesis, and narrative development.
• Design, operationalize, and scale repeatable AI-assisted workflows that support recurring company-level decisions (growth, margin optimization, inventory productivity, forecast alignment).
• Establish validation frameworks to pressure-test AI-generated outputs against source data, historical performance, customer behavior, and commercial logic.
• Embed AI-enabled insights into planning, forecasting, and executive performance reviews.
• Promote responsible, explainable, and outcome-driven AI usage across analytics workflows.
• Own web and digital analytics strategy using Heap, including funnel performance, customer journey analysis, and conversion optimization.
• Ensure tracking integrity and event schema accuracy, including review of structured data formats (e.g., JSON, data layers) in partnership with Product Engineering.
• Translate behavioral data into commercial implications that influence merchandising, marketing, inventory, and operational decisions.
• Ensure behavioral insights are aligned with downstream systems and financial realities.
• Reconcile demand, margin, inventory, and operational perspectives into cohesive performance narratives.
• Support planning, forecasting, and performance alignment across Finance, Retail, Merchandising, and Operations.
• Challenge assumptions and reframe business narratives using data-backed insight.
• Serve as a connector between commercial strategy and operational execution.
• Establish analytical standards that prioritize clarity, trust, commercial relevance, and decision impact.
• Reduce reactive ad hoc work by building scalable, decision-focused reporting and automation frameworks.
• Design the structure, workflows, and expectations for the analytics function.
• Hire, mentor, and develop junior analytics talent once foundational systems are established.
• Champion a modern, insight-driven analytics culture across the company.

Director of AI Analytics

• Plan, implement and administer the day-to-day operations of Data and AI Governance program.
• Support administrative functions for governance meetings, prepare materials, record decisions, track/close actions.
• Create and update playbooks, SOPs, decision logs, and audit-ready document repository.
• Confirm deployment readiness, monitor live systems, manage retirements, and maintain a central inventory and dashboard.
• Enforce internal AI policy, track local/state/federal AI rules and guidance, and support incidents, remediation, and audits.
• Gather adoption and outcome metrics and produce dashboards and one-page value summaries for leadership.
• Align internal and external messaging, prepare executive briefs, and support stakeholder and executive updates.

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