Summer Associate Internship

Team Overview: 

The Enterprise AI Strategy team sits within the Member Strategy Office (MSO) and accelerates the responsible adoption of Generative AI (GenAI) and Agentic AI across the enterprise. We drive use case discovery and delivery from ideation through proof-of-concept (POC), partnering closely with Business Units, Enterprise Data & Analytics (ED&A), and Enterprise Technology Services (ETS). In addition to building and validating value-driven use cases, we help activate enterprise programs—including Change Management, Knowledge Management, Training & Enablement, and Governance, Risk & Controls (GRC)—to ensure durable, compliant, and scalable AI outcomes. 

Potential Projects:

• Use Case Intake & Prioritization – Support intake, problem framing, value hypotheses, and scoring for GenAI/agent use cases aligned to MSO and BU priorities.
• POC Design & Evaluation – Help design small-scale experiments (e.g., prompt strategies, tool/agent orchestration, RAG pipelines), define success metrics, run evaluations, and synthesize results.
• Agentic Workflows – Prototype task-oriented agent flows (e.g., retrieval, planning, tool use, hand-offs) using enterprise-approved platforms.
• Knowledge & Change Activation – Create quick-start guides, job aids, FAQs, and training materials to enable pilot users and frontline teams.
• Governance, Risk & Controls – Contribute to model/use-case documentation, data handling checklists, and risk/benefit assessments to support responsible AI reviews.

Insights & Storytelling – Package findings into crisp narratives and executive-ready artifacts (dashboards, memos, readouts) for leadership decision-making.

The Summer Associate Program is a 12-week internship program beginning in May 2026 and ending in August 2026. Students will work on impactful projects and meaningful work during their internship. To qualify for this position, applicants must be currently pursuing a degree from an accredited college or university and have an anticipated graduation date of December 2026 or later.

• 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.
• 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.
• 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.

Preferred:

• Master’s candidate preferred.
• 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).
• Exposure to change management, training enablement, or knowledge management practices.
• Understanding of governance, risk, and controls considerations in AI (data privacy, model safety, compliance).

Hours: Monday – Friday 8:00AM - 4:30PM

Location: 820 Follin Lane, Vienna, VA 22180

 

• Discover & Frame: Support discovery workshops, stakeholder interviews, and problem framing to translate business pains into AI-addressable opportunities with clear KPIs.
• Design Experiments: Draft experiment charters (objectives, hypotheses, datasets, evaluation criteria); execute evaluations on prompts, retrieval strategies, and agent behaviors.
• Prototype: Build light POCs using enterprise-approved tools (e.g., prompt chains, evaluation harnesses, basic RAG components, agent/task flows).
• Data & Retrieval: Partner with data/knowledge teams to identify sources, structure corpora, and configure retrieval (metadata, chunking, embeddings) following data governance guidelines.
• Assess Value & Risk: Quantify impact (quality, time savings, risk reduction) and surface risks (bias, privacy, security) with mitigation recommendations aligned to GRC processes.
• Activate & Enable: Produce enablement content (quick starts, guides, demos) and support change management activities for pilots and early adopters.
• Communicate: Create clear summaries, visuals, and executive updates; present findings to AI Strategy leadership, MSO partners, and BU stakeholders.
• Operate Responsibly: Adhere to enterprise Responsible AI standards, data privacy requirements, and information security policies; use only approved platforms and non-production data.