Artificial Intelligence

• Own the full AI engagement arc from discovery through sustained workflow adoption
• Drive requirements and delivery precision to track against plans and surface risks early
• Champion continuous quality through testing and feedback loops during AI engagements
• Hold the R&D feedback loop accountable to ensure field learnings flow back
• Build playbooks to identify reusable patterns and scalable frameworks for the team
• Own engagement economics and track cost structure across active engagements
• This is a field-first role with expected travel to partner locations

AI Engagement Manager

• designing, building, and shipping AI‑powered products and platforms
• embedding strong technology risk and control foundations from day one
• hands‑on engineering, integrating, and hardening modern AI stacks
• defining how AI products are built, deployed, and scaled safely across Fidelity

AI Technology Risk Engineer

• Lead one or more AI solution deployments concurrently, including Workday AI products and acquired solutions
• Own end-to-end delivery execution, ensuring on-time, on-budget, and high-quality implementations
• Partner with Consulting, Product, and Customer AI teams to triage and resolve issues related to model performance
• Present at Executive Steering Committees, providing transparency on AI risks
• Support pre-sales activities as requested
• Coach and mentor Workday and partner resources in AI deployment practices
• Contribute to the ongoing evolution of the Workday AI Delivery Framework
• Facilitate the transition from deployment to steady-state operations

Senior Engagement Manager, AI Solutions Delivery

• 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

• Own end-to-end delivery for multiple enterprise customer engagements
• Partner with customers' C-level executives on AI strategy and transformation
• Lead one or more concurrent implementations, ensuring projects are delivered within the budget and timeline outlined in the statement of work
• Manage timelines, risks, and cross-functional coordination to ensure smooth delivery
• Identify retention opportunities and potential areas for expanded engagement
• Provide guidance and mentoring to Professional Services team members working on your implementations
• Identify and resolve issues that could affect project scope or timelines
• Participate in the sales cycle when requested to gather requirements, formulate delivery approaches, and develop proposals

Engagement Manager, AI Practice

• Be the domain credibility of the pod. You know grocery retail and CPG — merchandising, supply chain, store ops, trade, e-commerce — well enough to walk into any room at any level and earn it immediately. As the pod scales, you'll deepen into a vertical and become Instacart's sharpest expert in that space.
• Get to the real problem. Embed with partner teams to identify where agentic AI will actually move the business — not just what's technically possible, but what will change how operators work day to day. Translate those problems into AI-solvable use cases the pod can build against.
• Lead discovery and requirements. Run structured workshops at partner sites, surface and prioritize the use case map, and turn field learnings into clear inputs for the pod and R&D. You're the bridge between what partners need and what gets built.
• Show, don't just tell. Lead hands-on demos and platform walkthroughs tailored to the partner's specific workflows — making abstract AI capability concrete and compelling in the room. The ability to demonstrate the art of the possible is as important as the ability to articulate it.
• Own adoption. Drive the change management that makes deployments stick — new operating cadences, workflow redesigns, user training. Success is measured by whether team behavior changes, not whether software ships.
• This is a field-first role. Travel is a core part of the job — expect to be on-site at partner locations regularly, potentially 50%+ of the time. If you do your best work embedded with a team rather than behind a desk, you'll thrive here.

AI Field Consultant

• Lead one or more AI solution deployments concurrently
• Own end-to-end delivery execution
• Partner with Consulting, Product, and Customer AI teams
• Present at Executive Steering Committees
• Coach and mentor Workday and partner resources

• 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.

See all jobs on JobTailor

AI Engagement Manager

AI Technology Risk Engineer

Senior Engagement Manager, AI Solutions Delivery

Director, Enterprise AI Adoption

AI Enablement Intern

Engagement Manager, AI Practice

AI Field Consultant

Senior Engagement Manager, AI Solutions Delivery

VP Enterprise AI Enablement

Applied AI – Graduate Intern