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

• Create and curate a pipeline for sovereign AI across North America
• Explain HPE's solutions aligned with data security protocols to customers
• Provide input to product management and engineering
• Collaborate with sales teams to pursue opportunities requiring high security

Chief Technologist, HPC, AI

• 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

• 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

• Develop and maintain AI governance frameworks ensuring compliance with ethical, legal, and organizational standards
• Conduct risk assessments to identify potential harms, biases, or compliance gaps in AI models and workflows
• Collaborate with engineering, legal, and mission teams to ensure AI solutions align with governance policies
• Prepare, maintain, and execute a System Engineering Plan (SEP) for managing all systems architecture and system engineering aspects
• Design, prepare, and document systems engineering and cybersecurity artifacts for the System
• Conduct systems engineering activities to specify, build, and maintain system engineering designs
• Support the Government in recommending and conducting enterprise system architecture activities
• Define, document, maintain, and promulgate APIs and technical standards for the System
• Design, engineer, integrate, and continuously improve the underlying infrastructure of the System
• Identify, prepare, track, secure, and integrate government, commercial, and open-source tools and services into the System
• Design, architect, engineer, and continuously improve the user interface (UI) and user experience (UX) components
• Build and maintain services and products to make production-ready AI/ML models accessible for customer use
• Design, architect, engineer, and continuously improve all aspects of cybersecurity elements of the System
• Perform site reliability engineering to build and maintain a reliable, scalable, and efficient System
• Participate in the Engineering Control Board (ECB) process for supporting all major engineering milestones and decisions

SME AI Governance Specialist

• Collaborate with Sr. Consultant and business partners to identify AI opportunities
• Support translating proof of concept into scaled use and value creation using change management and project management best practices
• Work with the Sr. AI Consultant and business partners to create and groom portfolios of AI opportunities
• Facilitate the design of a proof of concept and testing plan
• Serve as an advisor and partner for change execution

Artificial Intelligence Consultant

• Evaluate candidate Large Language Models (LLMs), embedding models, and AI inference solutions for quality, latency, memory utilization, reliability, and operational performance on embedded GPU-enabled edge compute platforms, including the X9 Spider Mission Computer architecture.
• Tune and optimize AI model runtime configurations for edge deployment, including quantization strategies, batching configurations, context window sizing, cache behavior, inference scheduling, and GPU memory utilization specific to operational edge hardware environments.
• Collaborate with customer stakeholders to assess mission requirements and evaluate alternative edge compute platforms when operational demands exceed X9 Spider capabilities or when cost, performance, power, size, weight, or thermal tradeoffs require additional analysis.
• Benchmark agentic AI workflows, inference pipelines, and model-serving architectures against target hardware constraints and operational performance thresholds.
• Recommend model-selection, runtime, and configuration tradeoffs balancing mission effectiveness, latency, throughput, resource utilization, reliability, and operational sustainability.
• Build and maintain repeatable performance and stress-testing frameworks for evaluating latency, throughput, tool-call overhead, failover behavior, degraded-resource conditions, and disconnected operational scenarios on edge compute platforms.
• Package, deploy, validate, and sustain local model-serving components and inference services to support reliable operation within tactical and edge environments.
• Collaborate with agent engineers, AI developers, and integration teams to validate that agent behavior, workflow reliability, and operational outcomes remain acceptable following model compression, quantization, runtime optimization, or hardware configuration changes.
• Support deployment, troubleshooting, optimization, and sustainment activities for AI-enabled applications operating in edge, airborne, tactical, or disconnected operational environments.
• Train customer technical personnel on supported model profiles, operational constraints, runtime tuning considerations, deployment limitations, troubleshooting procedures, and platform sustainment best practices.
• Maintain technical documentation, benchmarking results, model validation reports, deployment procedures, optimization baselines, configuration guides, and operational support materials.
• Support DevSecOps and CI/CD activities associated with AI model packaging, deployment automation, runtime validation, and operational release processes.

Edge AI – Model Optimization Engineer

• Develop production-grade integration and automation solutions: Design and implement production-ready automation workflows that integrate cloud services, internal data sources, and LLM endpoints.
• Lead the development and deployment of domain-specific LLMs: Fine-tune lightweight models on internal data from Gong, Notion, BigQuery, and other sources, then make them accessible to teams across the organisation through intuitive interfaces.
• Establish best practices and patterns for AI and automation across the company: Create templates, documentation, and training materials that enable non-technical teams to build and deploy their own automation workflows confidently.
• Enable widespread adoption: Run hands-on workshops, provide ongoing support, and mentor teammates so anyone at UpGuard can leverage AI and automation tools effectively.
• Embed security & governance: Bake privacy, compliance, and cost controls into every workflow and model deployment, ensuring enterprise-grade reliability and data protection.

AI and Automation Engineer

• The AI and Data Assimilation Scientist will perform their job duties to a high standard, working both independently and collaboratively, focusing on scientific developments that advance the use of AI-DA techniques as an alternative to traditional ensemble/variational-based techniques.
• The core responsibility is to lead the development, implementation, testing, and evaluation of an AI-based Real-Time Mesoscale Analysis (AI-RTMA) system in support of NOAA’s National Blend of Models (NBM).
• The AI-RTMA system will generate high spatial and temporal resolution analyses of meteorological variables to reduce biases in the NBM fields.
• Communicate findings with EMC scientists and external partners to inform the development of a scientifically robust and efficient AI-RTMA approach.
• Collaborate with NOAA’s NBM team and key stakeholders to define product requirements for AI-RTMA, including domain configuration, grid structure, output variables, spatial and temporal resolution, and data formats suitable for operational evaluation and transition.
• Design, implement, and maintain robust data pipelines to support AI-RTMA training, validation, testing, and evaluation.
• Develop, train, rigorously test, and deploy a fully functional AI-RTMA system based on selected AI frameworks or architectures.
• Implement cross-validation and other evaluation methodologies to quantify model performance and reliability during inference.

AI Data Assimilation Scientist

• 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

• Support AI and data initiatives across the Company, including research, testing, and documentation
• Assist in exploring and evaluating AI tools and use cases aligned with the company's Data Strategy
• Help develop, test, and refine AI-driven workflows or automations
• Communicate findings and updates to internal stakeholders clearly and professionally
• Learn and work within internal platforms such as Snowflake, Smartsheet, and relevant AI/ML tools (Claude)

AI Intern

• 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

• Help to define and execute the AI strategy for R&D, aligned with organizational goals and innovation priorities
• Partner with R&D domain experts to identify high-value use cases and translate them into technical solutions
• Serve as a technical thought leader in applying AI/ML to life sciences, guiding project design, methodology, and implementation
• Lead complex, cross-functional initiatives by influencing and coordinating teams across organizational boundaries
• Drive adoption of AI tools and methodologies across R&D teams
• Partner with stakeholders to identify high-impact opportunities and translate them into scalable computational strategies and solutions
• Contribute directly to model development, data analysis, and algorithm design where appropriate, maintaining a hands-on technical presence
• Champion the integration of generative AI, large language models (LLMs), and foundation models into scientific workflows
• Establish best practices for data quality, model validation, reproducibility, and responsible AI use
• Influence the design of data architecture and pipelines to support robust AI/ML applications
• Evaluate emerging technologies and external collaborations to accelerate innovation
• Stay current with emerging AI technologies and assess their applicability to the organization and non-technical stakeholders
• Communicate complex technical concepts and insights clearly to diverse audiences, including senior leadership
• Represent the organization through scientific publications, conference presentations, and external partnerships

Director of R&D, Artificial Intelligence

• 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

• Lead the development and execution of strategic marketing initiatives to support portfolio growth and innovation
• Collaborate with product management, commercial teams, and regional stakeholders to deliver aligned marketing strategies
• Develop impactful content and campaigns to drive demand generation and strengthen market presence
• Execute integrated, multi-channel marketing programs to enhance customer value and engagement
• Position the organization as a thought leader across key markets through targeted messaging and insights
• Partner cross-functionally to drive business results

Global Market Development Manager, AI

• 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.

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