Artificial Intelligence

• Act as the go-to AI resource for your assigned team by meeting with stakeholders, understanding their workflows and pain points, and identifying where AI can provide practical value
• Triage incoming AI requests and determine the right path forward, whether that’s building a quick solution yourself, guiding someone to do it, or escalating to the broader AI or platform team
• Design and build low-code AI solutions such as prompts, workflows, agents, and rapid prototypes, then test, iterate, and refine them based on real user feedback
• Lead working sessions and provide hands-on coaching to help teams adopt AI tools, improve how they work, and build their own solutions with confidence
• Analyze AI usage and adoption trends to identify where teams are getting stuck and proactively deliver targeted support and enablement
• Contribute to firm-wide AI initiatives by collaborating with other AI Coaches, supporting internal programs and content, and helping execute trainings, events, and workshops
• Continuously stay current on emerging AI tools and capabilities while expanding your own skill set and sharing best practices across the firm

AI Coach – AI Enablement, Adoption

• 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 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

• 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

• 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

• 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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