Accelerate the responsible adoption of Generative AI (GenAI) and Agentic AI across the enterprise by driving use case discovery and delivery from ideation through proof-of-concept (POC), and activating enterprise programs for durable, compliant, and scalable AI outcomes.
Requirements
- Foundational understanding of LLMs/GenAI concepts (prompting, grounding/RAG, evaluation) and awareness of agentic AI patterns (planning, tool use, guardrails).
- Exposure to one or more relevant tools/languages: Python, SQL, notebooks, or low/no‑code orchestration; familiarity with enterprise AI platforms is a plus.
- Hands-on experience building small prototypes (e.g., prompt chains, retrieval pipelines, evaluation scripts) or experimenting with agent frameworks.
- Familiarity with vector search, embeddings, and basic evaluation methods (accuracy, precision/recall, qualitative rubrics, human-in-the-loop reviews).
- Understanding of governance, risk, and controls considerations in AI (data privacy, model safety, compliance).
Responsibilities
- Help design small-scale experiments (e.g., prompt strategies, tool/agent orchestration, RAG pipelines), define success metrics, run evaluations, and synthesize results.
- Prototype task-oriented agent flows (e.g., retrieval, planning, tool use, hand-offs) using enterprise-approved platforms.
- Support discovery workshops, stakeholder interviews, and problem framing to translate business pains into AI-addressable opportunities with clear KPIs.
- Draft experiment charters (objectives, hypotheses, datasets, evaluation criteria); execute evaluations on prompts, retrieval strategies, and agent behaviors.
- Build light POCs using enterprise-approved tools (e.g., prompt chains, evaluation harnesses, basic RAG components, agent/task flows).
- Partner with data/knowledge teams to identify sources, structure corpora, and configure retrieval (metadata, chunking, embeddings) following data governance guidelines.
- Quantify impact (quality, time savings, risk reduction) and surface risks (bias, privacy, security) with mitigation recommendations aligned to GRC processes.
Other
- Currently pursuing a Bachelor’s or Master’s in Computer Science, Data Science/Analytics, Information Systems, Human-Centered Design/HCI, Business/Technology, or related field; graduation Dec 2026 or later.
- Strong analytical thinking, structured problem solving, and communication/storytelling skills with the ability to simplify technical topics for business audiences.
- Collaboration mindset—curious, empathetic, and team‑oriented—with a bias for learning and action.
- Produce enablement content (quick starts, guides, demos) and support change management activities for pilots and early adopters.
- Create clear summaries, visuals, and executive updates; present findings to AI Strategy leadership, MSO partners, and BU stakeholders.
