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

• Own outcomes for programs/portfolios; establish delivery strategy, guardrails, and success metrics
• Drive execution from initiation through release and stabilization; manage scope, estimates, schedules, risks/issues
• Promote advanced Agile behaviors; coach teams toward self‑organization and continuous improvement
• Lead Implementation of best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies
• Serve as a trusted point of contact for sponsors and leaders; proactively manage risks and dependencies
• Mentor Scrum Masters/APOs; coordinate partner resources; uphold engineering and documentation standards
• Align delivery with technical design; leverage knowledge of modern data stacks (AWS, GCP, Snowflake, ETL/ELT, BI), Agentic solutions, graph database solutions, CI/CD, and DevOps
• Stay up to date with industry advancements in GenAI and apply modern technologies and methodologies to our systems
• Drive the culture of Innovative ideas to improve productivity, quality through GenAI, automation and process improvements

Data & AI Delivery Lead

• Serve as a strategic advisor across The Cigna Group senior clinical and business leadership, translating enterprise and organizational priorities into actionable strategies.
• Identify emerging trends in clinical care integrating AI, digital health, and other emerging technologies to inform business planning.
• Engage external industry leaders, prospective partners, and other thought partners for strategy development and benchmarking.
• Partner with business units, finance, and other operating partners to create and validate business cases, along with KPIs/success metrics.
• Create and iterate on high-impact presentations, analyses, and briefing documents for C‑suite and enterprise committees.
• Act as a catalyst for change, positioning The Cigna Group clinical team as a strategic growth engine within the broader enterprise.
• Support the program design and refinement of differentiated technology and AI offerings, tailored to specific clinical and business needs.
• Collaborate with matrix partners across Cigna business units, along with operational partners in technology and data and analytics to ensure alignment and execution.
• Ensure operational readiness and scalability of new clinical programs and services.
• Track and report on initiative performance, ensuring alignment with financial and clinical impact goals.
• Develop executive-level communications, including strategy memos and stakeholder updates.

Principal, Enterprise Clinical Technology & AI Strategic Initiatives

• Partner with stakeholders to understand business needs, operational pain points, and workflow inefficiencies across Marketing and Communications
• Analyze current-state workflows to identify opportunities for AI-enabled process improvement, automation, and redesign
• Serve as a consultant to teams on how to evolve ways of working through AI, agents, and automation
• Facilitate discussions that clarify problems, desired outcomes, and opportunities for workflow transformation
• Translate business needs into clear use cases, workflow requirements, acceptance criteria, and success metrics
• Assess solution feasibility, expected value, scalability, effort, and risk
• Prioritize opportunities in alignment with team needs, business impact, and the broader AI strategy
• Help manage a pipeline of solution opportunities from intake through recommendation and delivery
• Design, build, test, and deploy AI-enabled solutions including agents, automations, workflow improvements, and supporting tools
• Own implementation for assigned solutions, coordinating requirements, testing, stakeholder input, and rollout
• Iterate on solutions based on performance, feedback, and evolving business needs
• Maintain and enhance deployed tools and workflows to improve effectiveness and adoption over time
• Ensure solutions align with enterprise policies, governance requirements, and responsible AI practices
• Support teams in adopting redesigned workflows and integrating AI-enabled solutions into day-to-day operations
• Monitor usage, outcomes, and performance to identify gaps and opportunities for refinement
• Document reusable patterns, use cases, and recommendations that can be scaled across the function

Business Communications Advisor – AI Workflow and Solutions

• 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

• Own delivery, performance, and risk outcomes for a large, complex Claims Applied AI portfolio spanning multiple teams, domains, and value streams
• Drive measurable business value across the end-to-end claims journey by developing, testing, deploying, and scaling Predictive, Generative, and Agentic AI solutions
• Be the senior business partner for Claims leaders
• Build deep partnerships within portfolio and value stream frameworks
• Lead and develop senior leaders and teams
• Provide portfolio-level technical direction and oversight
• Ensure disciplined architecture and delivery trade-offs across quality, grounding, latency, cost, scalability, and regulatory risk
• Establish and enforce evaluation, monitoring, and production readiness across solution types
• Set governance expectations for unstructured data and retrieval across Claims
• Accountable for AI governance and compliance-by-design across the Claims portfolio
• Influence technology integration and platform strategy
• Champion reuse and scalability by partnering with AI platform owners and peers
• Provide thought leadership and change leadership
• Oversee portfolio planning, dependencies, resourcing, and financial stewardship

AVP Applied AI, Claims

• Lead stakeholders through process mapping and operational improvement
• Own the design, build, and iteration of low-code and AI-enabled workflows
• Translate business needs into reliable, production-grade automations
• Develop and maintain evaluation sets, define quality thresholds, and run A/B tests
• Integrate automation into dashboards and reporting
• Track and communicate impact of automation and AI solutions
• Continuously assess new and existing applications and SaaS platforms
• Lead pilots and proofs of value for new workflows or AI capabilities
• Provide training and enablement to Legal & Compliance teams
• Partner with the Compliance Training team to create AI use case specific policies

Data & AI Operations Manager

• 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 Enterprise AI Literacy Learning
• Governance for Learning Collaboration Team Tools & Processes
• Identify & Pilot Innovative AI Learning Tools

Director, Enterprise AI Learning, Governance, Innovation

• Lead the evaluation and production readiness of AI products and capabilities across the enterprise.
• Ensure solutions are scalable, secure, governable, and aligned with enterprise architecture standards.
• Partner closely with Legal and Information Protection to meet regulatory and risk management requirements.
• Conduct GenAI intake reviews, applying enterprise AI guardrails and standards.
• Partner with solution teams and area technology leaders to clarify requirements and reduce redundancy.
• Steward enterprise AI architecture principles and requirements during path-to-production reviews.
• Advise cross-functional teams on solution architecture and data protection optimization.
• Serve as a strategic advisor on enterprise AI capabilities and opportunities.

Senior Architecture Advisor – AI Enablement

• Define and articulate the vision, strategy, and roadmap for your portfolio of digital health insurance products.
• Work closely with architecture, design, content, engineering, legal, compliance, and analytics teams to develop and launch high-quality digital products.
• Advocate for the user by integrating customer feedback, usability testing, and market research into product development.
• Analyze performance metrics and user data to identify trends, inform product decisions, and optimize product features.
• Communicate product vision and updates to stakeholders, including executive leadership.
• Roll up your sleeves and guide teams to resolve business, design, and engineering problems quickly and efficiently.
• Lead agile epic and feature development and refinement processes, including PI planning, backlog management, and prioritization of features.
• Stay informed about industry trends, competitive landscape, and emerging technologies for your subject areas.

Senior AI Digital Product Manager

• Own delivery, performance, and risk outcomes for one or more large, complex Applied AI portfolios spanning multiple teams, domains, or lines of business.
• Translate enterprise and business-unit AI priorities into multi-year portfolio roadmaps and investment plans.
• Ensure applied AI solutions deliver measurable business value while meeting standards for security, reliability, explainability, fairness, safety, and cost efficiency.
• Lead and develop Sr. Directors and Directors.
• Build leadership bench strength through succession planning, coaching, and capability development.
• Provide portfolio-level technical direction and rigorous oversight, partnering closely with Principal ICs, Architecture, AI Platform, and Centers of Excellence.

AVP, Applied AI

• Own delivery, performance, and risk outcomes for one or more large, complex Applied AI portfolios spanning multiple teams, domains, or value streams.
• Translate enterprise and Small Business AI priorities into multi‑year portfolio roadmaps and investment plans.
• Ensure applied AI solutions deliver measurable business value while meeting standards for security, reliability, explainability, fairness, safety, and cost efficiency across solution types including generative and agentic AI, retrieval‑augmented systems, forecasting, recommendation systems, anomaly or fraud detection, and multimodal use cases.
• Lead and develop high performing teams of AI scientists and engineers.
• Build leadership bench strength through succession planning, coaching, and capability development.
• Ensure consistent application of the Applied AI operating model, decision rights, delivery discipline, and escalation paths across the portfolio.
• Reinforce shared expectations for quality, evaluation rigor, and production readiness.
• Provide portfolio‑level technical direction and rigorous oversight, partnering closely with Principal ICs, Architecture, AI Platform, and Centers of Excellence.
• Ensure consistent adoption of approved AI standards, patterns, and guardrails.
• Review and thoughtfully evaluate portfolio‑level architectural choices, evaluation approaches, production readiness, and operational risk signals, guiding leaders through disciplined trade‑offs across quality, grounding, latency, cost, scalability, and regulatory risk.
• Accountable for consistent application of evaluation and monitoring practices across the portfolio.
• Ensure evaluation frameworks span classification, information retrieval, RAG/chat, forecasting, and customer or operational KPIs.
• Oversee governance of metric taxonomies, thresholds, validation evidence, gold and synthetic test sets, A/B testing practices, drift detection, failure‑mode analysis, and incident response expectations.
• Ensure evaluation results inform prioritization, release decisions, and risk management at the executive level.
• Set portfolio‑level expectations and governance for unstructured data and retrieval practices, including document ingestion pipelines, parsing, OCR, layout‑aware extraction, metadata and lineage management, access controls, PII detection and redaction, and auditability.
• Ensure retrieval strategy decisions, including embedding approaches, hybrid and dense retrieval patterns, reranking, grounding validation, and multilingual considerations, align with enterprise standards and regulatory requirements.
• Accountable for portfolio-level AI governance ensuring alignment with Legal, Compliance, Model Risk, Privacy, Security, and Audit partners.
• Maintain readiness for audits and regulatory review by ensuring governance artifacts, controls, escalation paths, and operational evidence are consistently established and enforced.
• Escalate material risks, trade‑offs, and investment decisions to VPs with clear options and implications.
• Partner with senior leaders across the Business, Technology, Transformation Office, Finance, and HR to align Applied AI delivery with business outcomes.
• Influence portfolio funding, prioritization, and workforce planning through evidence‑based assessments of delivery performance, evaluation outcomes, and risk considerations.
• Oversee portfolio‑level planning, dependencies, resourcing, and financial stewardship.
• Adjust plans to address shifting priorities, capacity constraints, emerging technical risks, or regulatory changes.
• Drive continuous improvement in delivery effectiveness, operational resilience, governance maturity, and value realization across the Applied AI portfolio.

AVP Applied AI, Small Business

• Own end-to-end campaign execution: intake, planning, build, QA, launch, and post-launch reporting
• Write and edit campaign copy across channels — including emails, landing pages, and ad creative — adapting from existing content assets and messaging frameworks set by the Content and Product Marketing teams
• Schedule and lead campaign kickoff calls with clear agenda, roles, risks, and outcomes
• Build and manage Asana project trackers with tasks, timelines, dependencies, and owners
• Create campaign templates, naming conventions, workflows, and documentation
• Manage a single source of truth for campaign status, deliverables, approvals, and risks
• Work with Marketing Operations and RevOps to build dashboards tied to attribution, pipeline, and velocity
• Build AI agents to automate reminders, handoffs, briefs, approvals, QA checks, and post-launch updates
• Lead weekly campaign reviews, sharing status, blockers, and mitigation actions
• Coordinate timelines, QA, and deliverables with creative agencies and paid media partners
• Run post-campaign retrospectives, document learnings, and improve workflows and templates
• Support segmentation and audience targeting with PMM and operations teams
• Enable teams on new templates, workflows, and AI agents
• Review campaign performance and share insights with team to guide decisions, prioritization, and experimentation

Marketing Campaign Operations – AI Manager

• AI Solution Design & Development: Independently design and build complete AI solutions — agents, multi-step workflows, and integrations.
• Platform Configuration & Administration: Configure and manage AI platform environments including agent deployment, knowledge base architecture, data connector setup, access controls, and workspace organization.
• Data Orchestration & Integration: Connect, shape, and manage data pipelines between business systems (Salesforce, Totango, Jira, Redshift, Snowflake) and the AI platform.
• Technical Problem-Solving & Debugging: Independently diagnose and resolve complex issues across the AI solution stack.
• System Integration & Extensibility: Extend platform capabilities through available integration frameworks and develop custom connectivity solutions as needed.
• Workflow & Automation Development: Design and build automated workflows and multi-step processes within AI platforms.
• Program Champion Enablement: Partner with program champions embedded in Proofpoint's Services teams.
• Documentation & Knowledge Sharing: Create and maintain feature documentation, SOPs, capability guides.
• Business Consulting & Opportunity Identification: Proactively understand business workflows and identify AI opportunities.
• AI Landscape Evaluation: Track developments across the broader AI landscape and evaluate how emerging tools impact the team's work.
• Performance Monitoring: Monitor AI solution performance, identify improvement opportunities independently.

AI Integration Specialist

• Enhance project delivery through innovative approaches
• Lead teams in evaluating governance, risk, and control frameworks
• Mentor junior staff while confirming compliance and quality standards
• Build substantial client relationships through clear communication
• Utilize technology and data visualization to achieve project goals
• Identify opportunities for innovation and process enhancements
• Collaborate with stakeholders to maintain alignment on objectives
• Drive continuous improvement initiatives within the team

Senior AI Governance Manager

• Define and govern the strategy for AI products
• Lead teams of AI Product Owners
• Champion data quality, observability, performance
• Partner with Architecture, Governance, Engineering, InfoSec
• Establish and maintain operational readiness standards
• Partner with product, commercial, and business leaders
• Ensure teams have access to observability platforms
• Foster creative, resourceful product development
• Perform other related duties as assigned

Senior Director – AI Product, Enablement

• 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

• Develop and lead the HR AI change management strategy
• Partner with senior leadership to embed people-centered change practices
• Establish a framework for assessing AI-driven workforce impacts
• Define and measure key success indicators for AI adoption
• Lead efforts to help every employee understand AI impacts on their roles
• Create experiences that build confidence about AI adoption
• Partner with Communications and Learning teams to craft messaging
• Support leaders/guides through change with tools for adaptability
• Embed People Risk governance into AI delivery
• Celebrate success stories about AI adoption and human-machine collaboration

VP, HR – AI Enterprise Change Management

• 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.

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