Intern - AI Engineer

Nelnet Servicing•Lincoln, NE

About The Position

Nelnet's AI Lab is building the agent frameworks, integrations, and technical infrastructure that will define how Nelnet operates with AI over the next several years. AI Engineer Interns join a small, high-velocity team doing that work directly: building and testing agents, exploring LLM-based workflows, researching emerging frameworks, and contributing to production-bound tooling.

Requirements

Pursuing a degree in Computer Science, Software Engineering, Data Science, or a related technical field.
Has used LLMs for real technical work, not just experimentation. This might mean using Claude or ChatGPT to write, debug, or extend code; building a personal project with an LLM API; or integrating AI tooling into a workflow. Can speak concretely about what they built, how it worked, and what the limits were.
Python is the primary language. Solid working knowledge is required.
Personal projects, research, coursework projects, or prior internships that involved building something end-to-end. We want to see evidence of independently scoped and completed technical work, however small.
Experience working on a team where requirements weren't perfectly defined. Comfortable asking clarifying questions, proposing solutions, and moving forward without waiting for perfect information.
Uses LLMs as a genuine engineering tool, not just a novelty. Understands prompt construction, context windows, tool use, and the failure modes that matter in production. Knows when to reach for an LLM and when not to.
Understands how business workflows can be decomposed into agentic patterns: what an agent owns, what it delegates, what triggers it, and where it can fail. Asks the right questions about data access, identity, security, and trust before assuming the happy path.
Working Python proficiency. Comfortable reading unfamiliar code, debugging across system boundaries, and writing code others can maintain.
Can evaluate a new framework or approach systematically: what problem it solves, what the tradeoffs are, and whether it fits this team's constraints. Not satisfied by marketing copy or surface-level comparisons. Produces findings that are actionable.
Early-stage environments don't have complete specs. Comfortable breaking down a vague requirement into concrete next steps, identifying what needs to be true before moving forward, and flagging when a direction isn't working.
Can explain a technical approach to a non-technical stakeholder and a design decision to a senior engineer in the same day. Written communication is precise. Code and documentation reflect the same clarity as verbal explanations.
More interested in understanding how something actually works than in appearing to already know it. Experiments readily, is not defensive about being wrong, and learns faster from real usage than from documentation alone.
Understands that AI systems reflect design choices and that those choices have real consequences. Thinks seriously about data privacy, identity, and the responsible use of AI in an enterprise setting. Can engage with the tradeoffs, not just recite the principles.
The AI tooling landscape changes fast and so does this team's priorities. Comfortable updating assumptions when new information arrives. Not attached to the first approach when a better one emerges.

Nice To Haves

A strong portfolio of personal projects, open-source contributions, or prior technical internships carries more weight than GPA alone.
Experience with any of the following is a strong plus: LangChain, LangGraph, Claude API, OpenAI/Claude SDK, FastAPI, or similar frameworks.
Familiarity with REST APIs, JSON, and basic cloud or serverless patterns is beneficial.
Experience with LLM frameworks or API integration is a significant plus.

Responsibilities

Help build, test, and iterate on AI agents using the team's secure agent framework. This includes working with LLM APIs, multi-agent orchestration patterns, and tooling that runs in Nelnet's Microsoft 365 and cloud environment.
Track developments in LLM frameworks, agent architectures, tooling, and industry approaches. Evaluate options against the team's real constraints (security, identity, M365 integration) and surface findings with clear "so what" framing. Research here feeds real decisions, not slide decks.
Build proof-of-concept integrations between LLM-based systems and Nelnet's internal tools and APIs. Test assumptions early, document what worked and what didn't, and hand off something the team can build on.
Document design decisions, system behavior, and implementation notes with the precision of someone who expects a future engineer to read it. Participate in code review as both author and reviewer.
Present your work, findings, and recommendations to AI Lab leadership and team members at the end of the internship period.