Artificial Intelligence

• Define and evolve reference architectures for AI, machine learning, and generative AI systems
• Establish technical standards, patterns, and best practices for scalable and secure AI development
• Guide platform decisions across data, model development, and deployment infrastructure
• Evaluate and recommend tools, frameworks, and vendors aligned with long term architectural direction
• Design and implement end to end AI workflows for complex data interpretation, knowledge extraction, and reasoning
• Build systems that convert unstructured content into structured representations
• Develop and optimize retrieval approaches that combine semantic and lexical search techniques
• Prototype, evaluate approaches, test feasibility, and productionize generative AI solutions for real world use cases
• Partner with engineering teams to develop technology infrastructure
• Optimize systems for latency, cost, and throughput
• Design and implement evaluation approaches to assess performance, relevance, and effectiveness of AI solutions
• Apply techniques to improve consistency, accuracy, and trustworthiness of outputs
• Implement methods for scoring, explainability, and ongoing quality assurance
• Monitor deployed solutions and data pipelines for performance, stability, and data quality over time
• Incorporate responsible AI practices including transparency, bias mitigation, and risk controls
• Work closely with cross functional team to align data science initiatives with business priorities
• Partner with leadership to identify and prioritize high impact opportunities for data science applications
• Present insights and recommendations through clear visualizations tailored for audiences across different roles and expertise levels

AI Architect

• 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

• own the design, optimization, and evolution of our HR tech ecosystem
• develop the next wave of HR transformation through AI-powered automation
• design and execute upon a strategy for HR tech enablement which enhances efficiency, decision-making, and the employee experience
• review the end-to-end Workday platform
• execute the HRIS optimization roadmap aligned with business and HR strategy
• partner with business and HR leadership to translate organizational needs into scalable system solutions
• establish and oversee future HRIS operating model
• identify and deploy AI-driven solutions to streamline HR operations
• oversee maintenance and further implementation of AI-enabled tools
• evaluate AI use cases for governance, ethical guidelines, and risk controls for usage within HR
• oversee an integrated resource team

Director, HR Technology Transformation – AI Automation

• 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

• Own complete AI transformation projects end-to-end
• Manage direct reports through formal performance management
• Serve as primary client relationship owner
• Drive solution development through use case identification
• Proactively drive business development activities
• Oversee practice improvement initiatives
• Facilitate client workshops and capability building activities
• Manage project risks, issues, and changes proactively
• Provide formal coaching to team members

AI Solutions Consulting Lead

• Reporting to the Director of Communities and Ecosystem you are the primary commercial architect for SUSE’s most strategic AI relationships (NVIDIA, Intel, AMD)
• Lead the creation of the SUSE AI Factory narrative, developing technical GTM solutions that move at the speed of the AI market
• Identify and productize top use cases into validated "Blueprints" for OEM and GSI partners
• Architect the GTM strategy for our AI Developer Bundles to win mindshare at the workstation level
• Define and execute the "Better Together" narrative
• Author high-fidelity technical content
• Develop specialized training for global teams
• Lead SUSE’s technical presence at key AI shows using data-driven insights
• Assemble and lead a specialized team

AI Partner Marketing

• Execute and maintain Chamber’s enterprise AI roadmap
• Develop the analytical framework for AI’s impact
• Collaborate to bring AI tools to life
• Define how Chamber deploys AI responsibly
• Support Chamber’s broader strategic agenda
• Help shape how Chamber communicates its strategy

Director of AI Enablement

• Lead internal communications related to knowledge management, innovation, and AI initiatives
• Partner with Marketing & Business Development to support the firm’s external AI and innovation strategy
• Ensure consistent messaging across AI, innovation, and technology initiatives
• Develop and support a scalable change management function
• Serve as a strategic operations lead for the Knowledge Management & Innovation department
• Manage departmental planning, intake processes, project portfolio coordination, and operational governance
• Lead the strategy and development of the firm’s intranet and digital workplace platforms
• Lead the firm’s response strategy for AI-related client inquiries and RFPs

Director, AI Transformation

• 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

• Support the development and enhancement of financial forecasting and long-range planning models using AI-enabled tools and advanced analytics techniques.
• Analyze historical financial and operational data to identify patterns, forecast drivers, and opportunities for model automation or improvement.
• Partner with Finance and IT stakeholders to pilot and integrate AI tools into budgeting, forecasting, and scenario planning workflows.
• Assist in building scenario, sensitivity, and predictive models to support strategic planning and capital allocation decisions.
• Evaluate forecast accuracy and model performance; document assumptions, methodologies, and improvement opportunities.
• Support data preparation, cleansing, and validation activities to ensure model integrity and reliability.
• Develop documentation, templates, and user guidance for AI-enabled forecasting tools and models.

AI Forecasting Intern

• Own operational oversight, quality standards, and escalation management for AI-enabled member service processes
• Lead complex issue triage, root cause analysis, and corrective action planning
• Define and maintain operational validation standards, sampling approaches, and quality measures
• Lead requirements definition, prioritization, and validation for enhancements and releases
• Drive user acceptance testing, operational readiness reviews, and post-release validation
• Define, monitor, and communicate key performance indicators and operational insights to leadership
• Serve as the primary operational liaison across Operations, Quality, IT, Analytics, and vendor partners
• Mentor analysts and establish repeatable standards, documentation practices, and best practice

Senior Operations AI Analyst

• Execute daily operations of AI-enabled member service processes across multiple channels
• Monitor performance and review interaction outcomes for accuracy, quality, and compliance
• Analyze escalations and exceptions to surface trends, risks, and improvement opportunities
• Document business and functional requirements: workflows, procedures, user stories, acceptance criteria, and test scenarios
• Lead user acceptance and regression testing to validate operational readiness
• Maintain issue and defect logs; coordinate resolution with cross-functional partners
• Produce clear operational documentation, standard work, and runbooks
• Partner with Operations, Quality, IT, Analytics, and vendor teams on continuous improvement

Operations AI Analyst

• Lead the design and delivery of AI strategy engagements, including AI opportunity identification and prioritization.
• Operating model and capability design.
• AI governance, risk, and responsible AI frameworks.
• Roadmaps for AI adoption, scaling, and value realization.
• Translate complex AI concepts (e.g., generative AI, agentic systems, automation) into clear executive-level insights and decisions.
• Advise clients on aligning AI initiatives with business strategy, risk tolerance, regulatory considerations, and organizational readiness
• Own projects end-to-end, from scoping and planning through delivery and executive readout.
• Serve as the primary day-to-day client lead, managing scope, timelines, quality, and outcomes with minimal oversight.
• Coordinate with cross-functional teams (data science, engineering, risk, legal, cyber, operations) as needed to deliver integrated solutions.
• Proactively identify risks, dependencies, and decision points; escalate thoughtfully and early when required.
• Contribute to the development of AI frameworks, methodologies, accelerators, and points of view.
• Stay current on AI trends, regulations, and industry use cases; synthesize insights into practical guidance for clients and internal teams.
• Support business development through proposal contributions, client conversations, and thought leadership materials.
• Build trusted relationships with senior client stakeholders (Director, VP, C-suite).
• Identify follow-on opportunities and help shape future engagements based on client needs.
• Act as a visible representative of the firm’s AI capabilities and perspective.

AI Strategist

• 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

• Develop, design, and maintain enterprise data literacy, analytics, and AI training materials and curricula aligned to defined learning objectives and enterprise standards.
• Deliver engaging, learner-centered in-person and virtual training sessions using facilitation techniques that promote interaction, understanding, and real-world application.
• Translate complex data, analytics, and AI concepts into clear, accessible language for learners with varying levels of data literacy.
• Facilitate Insights Bar sessions, office hours, workshops, and learning events to support ongoing analytics and data enablement.
• Create training assets across multiple modalities, including instructor-led content, virtual learning, recorded video, self-paced e-learning, job aids, and interactive learning materials.
• Develop and deliver engaging training materials using modern instructional design tools and platforms, including Articulate Rise, Adobe Captivate, Storyline, JavaScript, video editing tools, and Learning Management Systems (LMS).
• Assist in developing and maintaining role-based training curricula aligned to defined personas (providers, analysts, developers, nurses, super users).
• Support standardized onboarding and enablement pathways across analytics, AI, and enterprise toolsets.
• Maintain training documentation, workflows, job aids, and multimedia learning assets in accordance with enterprise standards.
• Collaborate with subject matter experts and enterprise stakeholders to identify learning needs, clarify requirements, and translate them into effective training solutions.
• Provide remedial and optimization training to address adoption gaps or workflow challenges.
• Track participation, feedback, and effectiveness of training programs and communicate insights and recommendations to leadership.
• Coordinate training logistics, schedules, communications, and materials with stakeholders across clinical, operational, IT, and analytics teams.
• Perform other duties as assigned.

Data and AI Principal Trainer

• 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

• Process Discovery & Business Partnership
• Partner with security and legal teams to ensure all AI solutions are compliant with healthcare regulations, including HIPAA, and align with internal data security and privacy policies
• Conduct qualitative and quantitative analysis to define clear business requirements and translate operational challenges into actionable workflow designs
• AI Workflow Design & Engineering
• Architect and deploy AI-enabled workflows integrating internal systems, APIs, and third-party platforms
• Incorporate AI components such as LLM agents and retrieval pipelines
• Adoption, Training & Change Leadership
• Present workflow solutions to stakeholders and lead training sessions
• Act as an evangelist for AI adoption across the enterprise and contribute to AI playbooks and operational standards
• Establish telemetry for performance, drift, and cost
• Troubleshoot issues and identify issues and opportunities to improve workflow efficiency and additional automation

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