• Discover & shape high-value AI opportunities (internal process focus) • Partner with functional leaders to identify workflows where AI can remove friction, reduce cycle time, improve accuracy, or strengthen compliance. • Map the current state process, identify bottlenecks and failure modes, and re-design the process to be automation-ready (clarify inputs/outputs, decision points, data sources, controls, and ownership). • Define success metrics (time saved, error reduction, throughput, adoption, auditability) and translate business goals into a build plan. • Build internal AI agents and automation tools (end-to-end ownership) • Design and implement internal agents using modern LLM patterns (tool use/function calling, retrieval-augmented generation where needed, structured outputs, and human-in-the-loop checkpoints). • Build whole-product solutions: lightweight UX, service/API layer, integrations, data access, and automation triggers—appropriate to the use case. • Use AI-assisted development techniques to speed delivery while sustaining maintainability and readability. • Operate, maintain, and scale (production mindset) • Own reliability: monitoring, alerting, logging, incident response, and continuous improvements. • Establish repeatable patterns for onboarding new workflows and scaling existing ones (templates, shared components, evaluation harnesses, documentation). • Create and maintain runbooks and lightweight training so internal teams can adopt solutions confidently. • Risk, control, oversight, security & compliance by design • Implement appropriate guardrails: data minimization, access controls, secrets management, safe prompt/tooling patterns, output validation, and traceability. • Ensure solutions meet internal security and compliance expectations (including audit readiness, change management discipline, and clear ownership). • Maintain clear documentation of how systems work, what data they touch, and how risks are mitigated. • Cross-functional coordination • Coordinate across IT/Security, Legal/Privacy, and functional SMEs to get solutions approved and adopted. • Communicate progress with crisp updates; manage tradeoffs between speed and rigor. • Outcomes to be achieved • A portfolio of high-impact internal AI agents deployed into real workflows (not demos), with measurable business outcomes. • A scalable operating model for internal AI: reusable components, clear governance, and a predictable path from idea → production. • Reduced process friction through AI + process redesign, not AI bolted onto broken workflows. • High trust in outputs through appropriate controls, auditability, and operational reliability. • Working style / What success looks like here • You are low ego, high output: you can operate independently, but you collaborate naturally and bring others along. • You can move fast without being reckless: you know where to be scrappy and where to add rigor. • You care about outcomes: automation only matters if it changes how people work.

• Support the 711th Human Performance Wing (711 HPW) in developing an AI-powered Science Advisor tool. • Optimize the performance of the advisor tool by modifying system prompts and workflows. • Collect technical documents and literature, and test and evaluate model responses. • Participate in research seminars and showcase findings at a poster session.

• Shape AI engagement plans in key accounts, partnering with account teams and senior customer stakeholders. • Clarify customer AI goals and challenges and translate them into actionable roadmaps. • Help design or improve AI operating models and governance so proofs of value can become production solutions. • Align stakeholders across business, IT, data, and risk functions around AI priorities and decisions. • Represent Dell’s AI Factory strategy in executive discussions, together with Enterprise Architects and Sales leaders. • Lead or co-lead AI discovery, problem framing, and diagnosis with customer teams to uncover blockers to AI adoption and value realization. • Co-design AI operating models and governance (roles, decision rights, pilot-to-production processes) that support secure, compliant scaling of AI use cases. • Drive stakeholder alignment by mapping decision makers, facilitating workshops, and maintaining clear engagement cadences. • Partner with AI Architects and AI Data Specialists to define scope, success criteria, and readiness for AI proofs of value and production rollouts. • Define adoption and enablement approaches (change management, training, AI champions, communities of practice) that build long-term customer capability. • Coordinate FDE Unit engagement rhythms (checkpoints, steering sessions, risk reviews) with customer programs and Dell account leadership. • Provide feedback to Dell AI Factory offers and playbooks based on field experience and common customer patterns. • Mentor less experienced team members on AI governance, organizational change, and engagement best practices.

• Lead data readiness assessments to understand what data is available, how it is used, and how it can support AI use cases • Build and improve data pipelines and integrations that deliver AI-ready data for AI and GenAI workloads • Design and configure governance and compliance controls that enable secure and compliant use of data for AI • Help ensure that PoV data flows are designed with production scale, performance, and operations in mind • Partner with AI Architects, AI Strategists, and customer stakeholders to align data work with overall AI priorities and operating models • Perform hands-on data integration and transformation work to prepare AI-ready datasets for PoV and production solutions • Define integration patterns and pipelines that deliver AI-ready data with appropriate security, risk, and privacy controls • Ensure PoV pipelines are production-aware from the start, including patterns for monitoring, reliability, performance, and maintainability • Collaborate closely with AI Architects to align data flows with the AI software stack and avoid data-related blockers • Work with AI Strategists and customer stakeholders to align data work with AI governance frameworks, operating models, and stewardship processes • Contribute to documentation such as data flow diagrams, data catalogs, governance artifacts, and runbooks for AI data operations • Coach customer data engineers and platform teams on building and maintaining AI-ready data pipelines and governance practices