Artificial Intelligence

• The AI and Experience Orchestration Director leads strategic engagements, working closely with core account teams to accelerate adoption of Genesys native AI and Experience Orchestration capabilities.
• This role brings deep expertise in AI powered customer experience and helps clients translate business goals into impactful AI use cases that deliver measurable outcomes.
• Engage confidently with senior and C-level leaders and orchestrate complex opportunities across internal and external teams.
• Influence and guide deal strategy, partnering closely with account executives to support territory planning, pipeline creation, opportunity progression, and forecasting.
• Drive field execution of programs across the Genesys AI portfolio including sales campaigns and enablement initiatives.
• Serve as a trusted subject matter expert and model best practice sales motions that elevate capability across the wider team.
• Provide hands on support for positioning, presentations, pricing, proposals, and other key materials related to Genesys AI and Experience Orchestration.
• Lead customer workshops that shape AI use cases, prioritisation frameworks, and pilot opportunities.
• Act as the voice of the customer by capturing insights and partnering with cross functional teams in marketing, product, customer success, and professional services.
• Identify strategic priorities and develop AI led opportunity pathways aligned to customer objectives.

AI and Experience Orchestration Director

• Provide comprehensive capacity building and compliance assistance to AI/AN victim services programs
• Plan and execute assistance activities including developing and advancing customized assistance plans
• Assist programs in developing or enhancing victim services policies and procedures
• Support recipients in resolving OJP monitoring issues and implementing corrective actions
• Evaluate individual grantee needs and partner with grantees to implement customized assistance plans
• Develop and foster truly collaborative partnerships with tribal communities
• Facilitate trainings including webinars and peer learning exchanges
• Assist with strategic planning
• Identify and share culturally responsive, trauma informed practices

Senior Project Specialist – AI/AN Victim Services Programs

• Provide comprehensive capacity building and compliance assistance to Alaska Native victim services programs to ensure compliance with DOJ/OJP award conditions, including client confidentiality policies, program narratives, time-task plans, and addressing award conditions
• Plan and execute assistance activities including developing and advancing customized assistance plans, sharing subject matter expertise, and reporting on outcomes
• Assist programs in developing or enhancing victim services policies and procedures, conducting community needs assessments, and developing strategic and sustainability plans
• Support recipients in resolving OJP monitoring issues and implementing corrective actions
• Evaluate individual grantee needs and partner with grantees to implement and track progress of customized assistance plans
• Work closely to support grantees as they enhance and strengthen their crime victim services
• Develop trainings, curriculum, and resources appropriate for Alaska Native audiences and their identified needs
• Facilitate trainings including webinars, peer learning exchanges and communities of practice
• Assist with strategic planning
• Conduct reviews of resources, infrastructure, and capacity within the grantee communities
• Identify and share culturally responsive, trauma informed practices, resources, and approaches

Project Specialist – AI/AN Victim Services Programs

• Identify, evaluate, and prioritize opportunities to integrate AI into existing and new organizational processes and tooling
• Design and implement data structures, metadata models, naming conventions, and data standards to support AI and machine learning initiatives
• Support onboarding new AI projects by identifying data requirements, assessing data readiness, and defining ingestion and preparation workflows
• Develop processes to measure and improve ongoing data quality, consistency, completeness, and accuracy across AI datasets and workflows
• Build and maintain data preparation pipelines to support model training, testing, retrieval, and operational AI applications
• Collaborate with technical and business stakeholders across divisions to understand data sources, use cases, and operational constraints
• Create and maintain documentation for data standards, transformation logic, onboarding procedures, and quality controls
• Partner with AI, software, platform, and security teams to ensure data workflows are scalable, secure, and aligned with organizational objectives

Senior AI Technologist

• 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

• 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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