Artificial Intelligence

• 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 one or more AI solution deployments concurrently, including Workday AI products and acquired solutions
• Own end-to-end delivery execution, ensuring on-time, on-budget, and high-quality implementations
• Partner with Consulting, Product, and Customer AI teams to triage and resolve issues related to model performance
• Present at Executive Steering Committees, providing transparency on AI risks
• Support pre-sales activities as requested
• Coach and mentor Workday and partner resources in AI deployment practices
• Contribute to the ongoing evolution of the Workday AI Delivery Framework
• Facilitate the transition from deployment to steady-state operations

Senior Engagement Manager, AI Solutions Delivery

• 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

• Use proprietary software to provide labels, annotations, recordings, and inputs on projects involving multilingual audio clips, voice recordings, speech samples, and auditory elements in various languages.
• Support the delivery of high-quality curated audio data that ensures clear, natural spoken output, accurate representation of linguistic and prosodic details (such as intonation, rhythm, and accent), and professional audio standards.
• Collaborate with technical staff to develop tasks that improve AI's ability to handle speech modulation, accent variation, noise in real-world recordings, and multilingual audio processing.
• Work with technical staff to improve annotation tools for efficient audio workflows.

AI Tutor – Greek

• Provide labels and annotations on multilingual audio clips
• Support the delivery of high-quality audio data
• Collaborate with technical staff to improve speech processing
• Improve annotation tools for efficient workflows

AI Tutor, Hungarian

AI Tutor – Russian

AI Tutor – Swahili

• 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.

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.

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