Artificial Intelligence

• 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

• Build, test, and deploy automation, monitoring, and analysis solutions for agent workflows
• Support deployment and scaling of automated agent-based solutions across development and production environments
• Investigate technical, operational, and reliability issues during development and implementation
• Build and integrate software tools that improve system reliability, observability, and scalability
• Support setup of development and production environments, including deployment-related activities
• Develop best practices and methodologies for provisioning, configuration management, monitoring, and troubleshooting
• Collaborate with delivery teams to define and track service level indicators, service level objectives, SLAs, and error budgets
• Create and recommend process improvements that increase stability, efficiency, and operational readiness
• Contribute to the ongoing improvement of operational processes for automated agent workflows

Senior Artificial Intelligence Operations Specialist

• Own delivery of large and/or complex ML, DL, and Generative AI systems
• Design, implement, and evaluate AI systems for production
• Apply prescriptive analytics techniques to business problems
• Lead development and testing of ML algorithms
• Stay updated with advancements in AI/ML methods
• Benchmark against existing ML methods
• Develop prototypes and proof-of-concept implementations
• Lead consultation sessions with business partners
• Drive continuous improvement through novel techniques
• Identify actionable insights and communicate results

Senior AI/ML Scientist

• 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

• Define and enable the Power Analytics and Data Strategy in close partnership with the Power Data Science & AI and HQ Analytics teams
• Oversee AI Project Owners responsible for managing end-to-end product lifecycle, from ideation, deployment, to scaling
• Ensure successful deliver of AI solutions through vendor partners, AI Foundry, and internal stakeholders
• Drive agile product management practices tailored to AI/ML development
• Collaborate closely with AI Architecture teams on scaleable, enterprise-grade solution design, model development, AI readiness, and IT business leaders on integration and adoption
• Act as the primary delivery bridge between AI strategy team and technical execution/delivery
• Lead engagement with external vendors and delivery partners
• Ensure accountability for delivery quality, timelines, and outcomes
• Prioritize and optimize the analytics product portfolio across domains and business processes to drive strategic coherence, eliminate redundancy, and maximize business impact
• Translate business needs into actionable use-case roadmaps, balancing short-term value delivery with long-term foundational investments that strengthen analytics maturity and business self service leveraging AI tools and capability
• Support business stakeholders through the AI initiation process and collaborate with the Power Data Science & AI team once concepts are approved for Proof-of-Concept (POC)

Senior Director – Data and AI Delivery Leader

• 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

• Lead product development activities across the Enterprise AI & Data portfolio, collaborating with business and technical partners to ensure successful execution
• Direct and develop full-stack software development and UX design teams focused on delivering AI and analytics products
• Champion technical excellence, product thinking, continuous improvement, and knowledge-sharing across teams and programs
• Partner with business stakeholders to identify high-value use cases and translate needs into product roadmaps and deliverables
• Collaborate with data science, data engineering, architecture, security, and platform teams to ensure end-to-end solution alignment
• Support the development of GenAI, Manufacturing, Safety, Quality, and other enterprise business domain solutions
• Drive delivery of intuitive, scalable, and maintainable software products that enable data-driven decision making
• Oversee multiple initiatives and priorities across the product portfolio
• Facilitate communication across cross-functional teams and senior stakeholders to resolve issues, manage dependencies, and support decision-making
• Support agile planning, workflow management, metrics, and governance practices
• Ensure product development aligns with Boeing engineering standards, compliance requirements, and enterprise technology strategies
• Support talent development, performance management, and organizational capability building within the team

Senior Manager – Artificial Intelligence and Analytics Product Development

• Collaborate with data scientists, developers, and programmers to design and implement AI solutions.
• Assist in the creation of reference framework for practical application of AI.
• Support the development and deployment of AI solutions to solve real-world business problems.
• Participate in team meetings and contribute to project planning and execution.
• Projects created during the internship will make an impact on our business!

AI Pioneers Internship Program – Pionniers de l’IA

• 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

• Refine and implement Boeing’s AI Governance strategy, operating model, and multi-year roadmap
• Develop and lead a high-performing, multidisciplinary team to execute on program strategy, policy, controls, oversight, stewardship, and adoption across the enterprise
• Design risk-tiered governance approaches proportionate to use case sensitivity, business impact, technical complexity, and regulatory exposure
• Build practical intake, review, approval, exception, and escalation processes that enable responsible AI adoption while minimizing unnecessary delivery friction
• Drive alignment across AI governance, data governance, privacy, cybersecurity, and enterprise risk management practices
• Define organizational structure, decision rights, role clarity, and capability development pathways
• Foster a culture of enablement, accountability, integrity, and innovation consistent with Boeing’s Values
• Establish and maintain robust governance frameworks, standards, policies, and procedures, that are scalable, measurable, risk-based, and fit for use across a diverse portfolio of AI applications
• Prioritize strategic investments in tooling, workflow automation, and governance infrastructure that integrates effectively with IDT&S, engineering, and enterprise delivery process
• Develop OKRs, KPIs, and Metrics that measure adoption, control effectiveness, cycle time, risk reduction, cost avoidance, and business enablement, and provide executive level reporting on program impact and ROI
• Partner with data scientists, engineers, privacy, legal, and compliance teams to bridge innovation and risk management, with an enablement mindset, and embed AI governance requirements into AI development, validation, deployment, monitoring, and lifecycle change processes
• Develop governance approaches for traditional machine learning, generative AI, third-party AI services, and vendor-enabled AI capabilities
• Define documentation, traceability, validation, and lifecycle evidence standards, including model cards, data lineage, validation artifacts, human oversight requirements, and change records, to support compliance, assurance, and audit readiness
• Develop and deliver general and role-specific training, communications, and change management strategies which build awareness, accountability, and practical adoption
• Stay abreast of emerging trends and best practices in AI Governance and related topics to ensure the organization remains at the forefront of industry standards

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