Artificial Intelligence

• Lead the design, development, and deployment of high-impact AI solutions across business lines
• Drive rapid experimentation, prototyping, and pilot execution, converting innovations into enterprise-scale deployments
• Move ideas from experimentation and prototyping into production-ready, commercially viable, enterprise-scale capabilities
• Oversee priority solution domains, including agentic AI, document intelligence, advanced models, and generative AI applications
• Own delivery of measurable outcomes, including revenue growth, cost optimization, efficiency gains, risk reduction, and customer or employee experience improvements
• Lead multi-disciplinary teams across ideation, validation, deployment, scaling, and optimization.
• Partner with technology and product organizations to industrialize AI delivery and establish reusable patterns, platforms, and capabilities
• Maintain strong regulatory, risk, ethical, and Responsible AI practices, proactively addressing bias, model risk, privacy, security, and unintended consequences.
• Provide leadership across the full AI lifecycle including in Ideation and early-stage R&D, Experimentation and validation, Production deployment, Enterprise scaling and optimization
• Serve as a senior advisor and integrator across decentralized AI initiatives, ensuring alignment to enterprise strategy and investment priorities
• Collaborate with business lines, product, technology, risk, legal, compliance, and data teams to embed governance and controls into delivery
• Drive enterprise adoption through leadership education, community engagement, “art of the possible” sessions, and use-case discovery
• Build, empower, and harmonize multidisciplinary teams across data science, engineering, product, risk, legal, compliance, and business functions
• Create a culture of innovation, experimentation, accountability, reuse, and continuous learning

Head of AI Delivery

• Own the Data & AI Product Lifecycle (End-to-End)
• Act as a True AI Product Owner (Hands-On)
• Lead Data Product & Modeling Strategy
• Deliver AI Products that Drive Advisor Productivity
• Drive Adoption & Value Realization
• Lead and Develop a High-Performance Team

Director, Data & AI Products

• Own the enterprise AI feasibility function, providing authoritative guidance on Model selection (traditional ML, GenAI, agentic AI)
• Approved platforms, tools, and services
• Architectural patterns and trade‑offs
• Act as the primary technical advisor to business and technology teams evaluating AI use cases.
• Ensure teams are guided toward enterprise‑approved solutions that accelerate delivery and reduce long‑term operational risk.
• Identify delivery blockers early (data readiness, platform onboarding, governance dependencies) and drive resolution.
• Establish and maintain AI playbooks, standards, and best‑practice frameworks for internal teams.
• Sponsor and guide enterprise‑wide workshops, training, and knowledge‑sharing initiatives.
• Provide consultative guidance on AI solutions and architecture, partnering with product, engineering, and business teams to shape use cases, design patterns, and implementation approaches.
• Act as a trusted advisor to teams evaluating AI feasibility, trade‑offs, and architectural alignment with enterprise standards.
• Build and lead an AI Center of Excellence to drive talent strategy, mentorship, and continuous capability development.
• Provide leadership and oversight for enterprise MLOps practices, including CI/CD, model registry, and automated rollback.
• Ensure AI systems meet regulatory, security, and privacy standards in collaboration with risk and compliance stakeholders.
• Define and review SLAs, KPIs, and observability standards for AI services, ensuring operational excellence and accountability.
• Set the architectural vision for a scalable, modular AI ecosystem spanning data ingestion, feature stores, training infrastructure, and inference.
• Champion standards for model governance, including versioning, data lineage, explainability, and auditability.
• Evaluate emerging AI technologies and approaches, and define adoption roadmaps aligned with business value and risk tolerance.
• Own the strategic evolution of the on‑prem AI platform stack (e.g., Airflow, Elasticsearch) and its operating model.

AI/ML Deployment Leader

• Manage a defined commodity portfolio or support senior traders with direct accountability for performance and risk exposure
• Develop and execute trading strategies within approved risk limits
• Actively manage price, basis, and structural risk
• Lead or contribute to global supply and demand modeling, scenario analysis, and market point-of-view development
• Apply statistical, quantitative, and AI-driven techniques to improve forecasting and identify trade opportunities
• Translate complex market data into actionable trading strategies
• Build and maintain analytical models, dashboards, and decision tools to support trading and risk management
• Automate data pipelines and improve speed and quality of market insights
• Collaborate with sales, logistics, finance, and risk teams to execute trades and optimize outcomes
• Engage with customers, suppliers, and counterparties to support commercial growth
• Influence broader trading strategy and contribute to team development of market POV

Commodity Trader – Agronomy, AI & Quantitative Strategy

• Develop and maintain Vizient’s enterprise AI tooling strategy and roadmap aligned with AI-DLC, architecture, security, governance, product, and delivery priorities.
• Identify tooling gaps, duplication, interoperability issues, adoption barriers, operational risks, and consolidation opportunities.
• Recommend tooling priorities, investment needs, lifecycle actions, and rationalization opportunities to leadership.
• Own the enterprise lifecycle framework for AI tools from evaluation and onboarding through adoption, optimization, renewal, and retirement.
• Establish standards for AI tool intake, ownership, onboarding, access model definition, integration readiness, documentation, observability expectations, support readiness, lifecycle reviews, and retirement.
• Maintain an enterprise inventory of AI tools, capabilities, owners, users, licenses, integrations, costs, risks, controls, vendors, and operational status.
• Partner with Architecture, Security, Infrastructure, Governance/Compliance, Procurement, Legal, Finance, Product, Engineering, Platform, LLMOps/AIOps and vendor teams to align AI tooling with enterprise requirements.
• Coordinate vendor and platform engagement for AI tooling, in partnership with Procurement, Legal, Finance, and accountable platform owners, including vendor assessments, support models, escalation paths, roadmap awareness, renewal planning, and performance tracking.
• Track AI tooling spend, license utilization, adoption patterns, renewal timelines, risks, and cost optimization opportunities.
• Facilitate the intake, prioritization, and cross-functional review of AI tooling requests, lifecycle decisions, implementation dependencies, and operational readiness needs.

AI Tooling Strategy – Lifecycle Management Director

• Lead the organization’s AI strategy aligned with business goals and long-term innovation objectives
• Identify opportunities where AI and machine learning can improve operations, decision-making, customer experience, and organizational performance
• Establish and maintain a prioritized roadmap for AI initiatives across the organization
• Serve as the organization’s subject-matter expert on artificial intelligence and related emerging technologies
• Advise executive leadership on AI capabilities, opportunities, risks, and investment priorities
• Lead the design, development, and deployment of AI and machine-learning solutions
• Oversee the full lifecycle of AI initiatives including opportunity assessment, solution design, model development, testing, deployment, and ongoing optimization
• Ensure AI solutions are scalable, secure, and integrated with existing data and technology systems
• Guide the selection and implementation of AI tools, platforms, and technologies
• Measure adoption and business impact resulting from AI solution implementation
• Build, lead, and mentor a team of AI specialists
• Establish best practices for AI development, model management, and data workflows
• Foster a culture of innovation, collaboration, and continuous learning within the AI team
• Collaborate across IT and data teams to ensure the organization has the data infrastructure required to support AI initiatives
• Promote strong data governance, data quality, and data management practices
• Drive adoption of AI capabilities across the organization and measure ongoing business impact
• Establish policies and best practices for responsible and ethical AI use

Director of AI

• 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.
• 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 and implement the enterprise AI operating model, including governance forums and funding processes.
• Establish enterprise standards for responsible AI, data usage, security, privacy, and risk management.
• Own AI investment governance, ensuring alignment with capital planning and transformation funding.
• Own the global AI use case portfolio and prioritization framework.
• Define, track, and report enterprise AI value metrics.
• Orchestrate cross-functional delivery across IT, data, and business units.
• Partner with HR to lead enterprise-wide change management and AI literacy initiatives.
• Build, lead, and develop a high performing enterprise AI organization with clear roles.

VP, Enterprise AI Enablement

• Lead the development and execution of the Tech+Talent 2026 marketing strategy to achieve key business objectives.
• Utilize market research to identify trends, customer needs, and insights that shape and inform our marketing tactics.
• Design and implement innovative marketing campaigns tailored to targeted customer segments, ensuring alignment with strategic goals.
• Develop clear and compelling solution positioning and messaging that differentiates Axiom's offerings with a specific focus on Axiom’s AI capabilities.
• Collaborate with strategy, technology, and commercial teams to convert capabilities into customer-centric benefits and articulate these in marketing collateral.
• Create engaging sales enablement materials, including solution briefs, presentations, and case studies to support sales efforts.
• Drive the execution of our marketing plan for new solution launches, ensuring seamless integration across multiple digital channels.
• Coordinate with marketing communications to deploy campaigns that drive brand awareness and increase demand generation.
• Analyze marketing campaign performance and leverage data-driven insights to refine and optimize strategies continuously.
• Work closely with sales teams to capture customer insights and refine solution selling approaches.
• Engage with strategy teams to deliver market feedback that guides prioritization of initiatives.
• Collaborate with commercial teams to harness customer experiences in marketing narratives and success stories.
• Maintain detailed project plans, timelines, and documentation for marketing initiatives.
• Identify and mitigate potential risks to ensure successful program delivery.

Senior Manager, AI Solution Marketing

• Lead design of enterprise AI architecture and reusable platform capabilities
• Define architectural standards, governance models, and best practices for AI implementations
• Evaluate and recommend AI platforms and tooling such as Azure AI services, AWS AI/ML platforms, OpenAI-based solutions, and enterprise LLM platforms
• Develop architecture roadmaps aligned to business strategy and enterprise technology direction
• Guide engineering teams in implementing scalable AI solutions leveraging cloud-native architectures, APIs, and distributed systems
• Design solutions with sufficient technical depth to support implementation and operationalization
• Identify risks and ensure alignment with security, identity, network, and data governance requirements
• Influence decisions around model deployment strategies, integration patterns, and enterprise AI platform design

Senior AI Technology Architect

• Supports the Business Banking Sales organization by identifying, prioritizing, and implementing AI-driven solutions
• Enhances sales effectiveness, operational efficiency, and client experience
• Collaborates cross-functionally to develop, pilot, and scale AI & ML initiatives
• Monitors and measures the impact of implemented solutions
• Tracks adoption metrics and business outcomes, providing regular updates to leadership
• Prepares executive-level presentations and status updates

Business Banking Leader – AI, Process Optimization

• Lead the design, architecture, development, and deployment of intelligent automation solutions using Microsoft Power Platform and UiPath technologies
• Serve as a subject matter expert for automation platforms, providing guidance on governance, scalability, best practices, and solution optimization
• Partner with business stakeholders and cross-functional teams to gather requirements, assess business processes, and identify automation opportunities
• Develop and maintain solutions utilizing Power Apps, Power Automate, Power BI, Dataverse, AI Builder, UiPath Studio, UiPath Orchestrator, and related technologies
• Drive automation and modernization initiatives by transforming manual and repetitive processes into scalable, efficient, and reusable solutions
• Lead technical delivery activities including solution design, development, testing, deployment, production support, and continuous improvement
• Ensure automation solutions adhere to enterprise standards for security, reliability, maintainability, and performance
• Collaborate with technical teams to integrate automation platforms with APIs, databases, cloud services, enterprise applications, and third-party systems
• Troubleshoot and resolve complex technical issues involving workflows, integrations, automation orchestration, and platform performance
• Lead automation projects and workstreams, managing timelines, priorities, deliverables, risks, and stakeholder communications
• Develop reporting, dashboards, and analytics solutions that improve operational visibility and support data-driven decision making
• Communicate technical concepts, recommendations, and project updates effectively to both technical and non-technical audiences
• Mentor automation engineers, developers, and analysts through coaching, technical guidance, peer reviews, and knowledge sharing
• Support the advancement of automation governance frameworks, standards, and Center of Excellence (CoE) practices
• Research and evaluate emerging automation, AI, and low-code technologies to drive innovation and continuous improvement

Lead Analyst, AI Intelligent Automation

• Lead enterprise AI operational activities, including runtime monitoring, operational support, incident management, production reliability, and operational continuity for AI-powered applications and intelligent automation solutions.
• Establish, implement, and continuously improve AI operational practices, including AIOps and LLMOps processes, runtime observability, operational telemetry, drift detection, release coordination, support workflows, and operational readiness activities.
• Drive runtime stability and service reliability initiatives through production monitoring, escalation management, root cause analysis, operational playbooks, and service continuity practices.
• Support enforcement of runtime governance standards, operational safeguards, human oversight controls, and secure operationalization practices for enterprise AI solutions.
• Ensure operational excellence across AI environments through proactive monitoring, issue prevention, and continuous service improvement efforts.
• Lead initiatives focused on runtime efficiency, operational scalability, inference utilization, supportability, performance optimization, and sustainable AI operations.
• Promote standardized operational processes, scalable support models, automation opportunities, and continuous improvement initiatives across AI operations functions.
• Drive operational maturity by identifying opportunities to enhance performance, reduce operational risk, and improve support effectiveness.
• Partner closely with AI Engineering & Delivery, AI Governance, AI Quality Engineering, Automation, Architecture, Platform Engineering, Security, Infrastructure, and business stakeholders to ensure operational readiness and runtime reliability.
• Coordinate operational execution activities across AI operations teams, including operational planning, vendor and contractor management, issue prioritization, escalation management, knowledge transfer, and delivery continuity.
• Support operational assessments, production readiness reviews, implementation planning, runtime support strategies, and modernization initiatives for prioritized AI capabilities.
• Collaborate with technical and business leaders to align operational practices with enterprise AI objectives and service expectations.
• Lead, mentor, and develop operations managers, engineers, analysts, and contractor resources while fostering a high-performing, collaborative, and continuously learning culture.
• Provide clear communication regarding operational performance, runtime risks, service reliability concerns, optimization opportunities, engineering tradeoffs, and strategic recommendations.
• Establish accountability for operational outcomes while promoting operational discipline, innovation, and continuous improvement.
• Research and evaluate emerging AI operational technologies, observability platforms, automation capabilities, optimization techniques, and runtime management practices to drive innovation and operational effectiveness.

Director, AI Operations – Optimization

• Drive product strategy and execution across a portfolio of no-code and low-code AI products and workflow automation capabilities, with a focus on improving operational efficiency, strengthening controls, and delivering measurable business value.
• Partner with product leadership to define strategic priorities, clarify objectives, and translate business goals into actionable roadmaps, operating plans, and execution routines.
• Identify opportunities to create business value by assessing operational challenges, process inefficiencies, stakeholder needs, and potential technology-enabled solutions.
• Connect work across teams, products, and business groups to identify synergies, reduce duplication, and improve alignment across the product portfolio.
• Support innovation efforts by helping evaluate new ideas, structure exploratory work, define success criteria, and ensure experimentation is connected to measurable business outcomes.
• Establish and manage operating routines, including leadership cadence, portfolio reviews, intake forums, prioritization discussions, status updates, and executive-ready communications.
• Develop frameworks, materials, and reporting that help leadership make informed decisions on strategic priorities, resources, risks, dependencies, delivery progress, adoption outcomes, and business value.
• Partner with product pods to anticipate and remove execution roadblocks, manage dependencies, and support alignment across business, product, technology, risk, and control stakeholders.
• Support resource planning, demand management, intake prioritization, and portfolio-level visibility to help ensure the team is focused on the highest-value initiatives.
• Prepare clear, concise, and well-structured communications for leadership and stakeholder audiences, including strategic updates, decision materials, meeting summaries, adoption materials, and portfolio narratives.
• Use data, metrics, stakeholder feedback, and business outcomes to assess progress, identify improvement opportunities, and support continuous improvement across the product organization.
• Promote a culture of collaboration, accountability, operational excellence, risk awareness, adoption discipline, and continuous learning.

Lead Strategy & Planning Consultant – AI Product Strategy, Innovation

• Own end-to-end execution of AI initiatives across Consumer & Business Banking, from problem definition through implementation and value realization
• Translate business priorities into a clear, actionable AI roadmap aligned to financial, risk, and strategic outcomes
• Establish and manage structured intake, prioritization, and governance processes
• Drive portfolio alignment, eliminate duplication, and enable reuse of scalable AI capabilities across business lines
• Lead redesign of end-to-end business processes, embedding AI, automation, and analytics into core workflows
• Ensure AI solutions are integrated into frontline workflows to drive measurable productivity and client experience improvements
• Proactively identify and prioritize AI and automation opportunities that improve business performance and client engagement
• Serve as the primary point of accountability for AI execution within Consumer & Business Banking

AI Transformation Director

• Serve as deputy to the Enterprise Knowledge Management leader, helping define and execute the enterprise knowledge strategy, roadmap, and operating model.
• Drive the convergence of knowledge management and content management, ensuring a cohesive approach across structured and unstructured information.
• Translate business needs into scalable knowledge solutions that support enterprise-wide adoption.
• Lead the application of AI to knowledge and content, including discovery, summarization, classification, tagging, and retrieval.
• Design and implement contextualization strategies so AI systems surface the right knowledge, to the right people, at the right time.
• Own the knowledge architecture within Microsoft 365, with deep expertise in SharePoint, Teams, copilot search, metadata, and integration patterns.
• Optimize M365 as a knowledge platform, including governance models, information architecture, and user experience.
• Lead information lifecycle management practices, including creation, classification, retention, archiving, and disposition.
• Ensure knowledge and content governance balances compliance, risk management, and usability.
• Act as a trusted advisor to senior stakeholders on knowledge, content, and AI capabilities.

Knowledge Management Associate Director – AI Enablement

• Establish and evolve Responsible AI strategy
• Oversee AI governance structures and practices
• Develop automated risk assessments and monitoring
• Partner closely with business units
• Drive engagement and accountability for AI best practices
• Lead and manage a 30-person team

Vice President, Responsible AI

• Champion Agile principles and practices across AI and data-focused teams, driving adoption and continuous improvement
• Coach product owners, engineers, and business stakeholders on Agile frameworks such as Scrum, Kanban, or SAFe
• Facilitate core Agile ceremonies (sprint planning, stand-ups, reviews, retrospectives) to ensure effective team execution
• Foster a culture of transparency, collaboration, and iterative delivery across cross-functional teams
• Partner with technical teams (data science, ML engineering) and business leaders to translate AI use cases into actionable backlogs and sprint goals
• Guide teams in delivering AI/ML solutions iteratively, accounting for experimentation, model training, and validation cycles
• Align delivery plans with business objectives, ensuring AI initiatives provide measurable value
• Support prioritization of work based on business impact, technical dependencies, and data readiness
• Enable collaboration across diverse teams, including data scientists, engineers, product managers, and domain experts
• Remove impediments and escalate risks impacting delivery timelines or outcomes
• Act as a liaison between technical and non-technical stakeholders to ensure shared understanding
• Define and track Agile and AI-specific performance metrics (e.g., velocity, cycle time, model performance indicators)
• Ensure AI delivery adheres to governance, compliance, and ethical standards (e.g., bias, data privacy)
• Facilitate retrospectives and apply learnings to improve delivery effectiveness and team maturity
• Promote data-driven decision-making and experimentation
• Support enterprise Agile transformation efforts, particularly within AI and digital initiatives
• Influence leaders and stakeholders to adopt Agile ways of working and AI-enabled delivery models
• Drive mindset shifts toward iterative development, experimentation, and continuous learning

AI Agilist

• Lead a cross-functional product pod of Solution Consultants, Pega Configurators, and Business Analysts to deliver Pega-based workflow automation solutions aligned to business priorities.
• Provide day-to-day product direction, delivery oversight, mentorship, and issue resolution support to help the team meet commitments and maintain high execution standards.
• Own solution delivery from discovery and design through configuration, testing, deployment, and post-implementation feedback, with a focus on quality, usability, scalability, and control readiness.
• Work hands-on within Pega App Studio and Dev Studio to support solution design, configuration, testing, and implementation of workflow capabilities.
• Partner with architecture and engineering teams to ensure solutions align with platform architecture, technical standards, security requirements, and software delivery lifecycle expectations.
• Translate operational pain points, process inefficiencies, and business needs into clear product requirements, workflow designs, user stories, acceptance criteria, and delivery plans.
• Partner directly with operational leaders and business stakeholders to assess opportunities, shape solution options, manage expectations, and drive alignment on scope, priorities, benefits, and delivery timelines.
• Establish and monitor success measures for delivered solutions, including adoption, operational efficiency, user experience, quality, risk reduction, and other relevant business outcomes.
• Elevate product management practices across the team by improving intake, prioritization, roadmap planning, stakeholder communications, and outcome measurement.
• Identify and help resolve risks, issues, impediments, and dependencies that could impact delivery quality, control effectiveness, or business outcomes.
• Foster a collaborative, inclusive, and accountable team environment that supports talent development, clear communication, continuous improvement, and operational excellence.

Pega Product & Solution Delivery Lead – AI Case Management

• Assist with defining and executing the organization’s AI adoption roadmap, aligning with business objectives.
• Lead development of AI agents, workflows, and prompt engineering to automate and optimize processes.
• Establish governance for responsible AI use, ensuring compliance, security, and ethical standards.
• Evaluate emerging AI technologies (Copilot, Azure AI, AI Foundry) and integrate them into enterprise solutions as agreed.
• Lead in establishing a Centre of Excellence. Promote best practices, reusable components, and knowledge sharing.
• Support teams by leading discovery workshops, requirements gathering, and solution design sessions.
• Create reusable AI components and frameworks to accelerate delivery across teams.
• Design, develop, and implement end-to-end solutions using Power Apps, Power Automate, Power BI, and Dataverse.
• Manage and enhance governance tools like the CoE Toolkit.
• Integrate Power Platform with Microsoft 365, Salesforce, NetSuite, GTM systems, and other business tools.
• Develop a community to support AI adoption across the business to enable knowledge sharing and effective delivery.
• Partner with business units to identify opportunities for AI-driven transformation.
• Support UAT, deployment, training, and post-go-live troubleshooting for AI and Power Platform solutions.
• Document AI architecture, workflows, security roles, and governance models.
• Ensure all solutions meet accessibility, security, and compliance standards.

AI Lead – Power Platform

• Lead the strategy, engineering, and applied AI capabilities that power search, discovery, and product finding platforms across digital channels.
• Drive measurable business outcomes through relevance optimization, search platform modernization, machine learning, GenAI, and agentic commerce capabilities.
• Implement the technology strategy and execution for GPC’s unified commerce platforms.
• Exhibit a deep understanding of modern technology stack and agile delivery models.
• Partner with Product and cross-functional GPC teams to define and execute a vision for AI-powered search that improves conversion, revenue, and customer productivity.
• Implement the technology platforms architecture and execute on roadmap to support build out the new commerce related capabilities needed for the unified commerce strategy.
• Lead the engineering teams responsible for search platform architecture, scalability, reliability, and performance.
• Drive modernization of search infrastructure leveraging cloud-native and AI-enabled technologies.
• Build and scale Search Science capabilities including relevance tuning, ranking models, semantic search, vector search, recommendations, and personalization.
• Responsible for delivery and support of the platform.

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