AI / ML Engineer

Nelnet Servicing•Lincoln, TX

1d•Hybrid

About The Position

Nelnet is a diversified and innovative company committed to enriching lives through the power of service. At Nelnet, we believe in the transformative power of data and technology to drive business innovation. Our AI / ML Engineer role sits at the center of that work: building, operating, and scaling the AI agents that automate processes, surface insights, and help our teams make better decisions faster. If you're passionate about taking agentic systems from prototype to dependable production capability, we invite you to join our team and help shape how AI gets used across our business. This posting covers multiple levels within the AI / ML Engineer role. We are currently seeking candidates at the equivalent of a Level I or II AI / ML Engineer. The responsibilities and qualifications below describe the full scope of the role; expectations for depth, autonomy, and scope of ownership will vary by level. Level and corresponding compensation are determined during the interview process based on demonstrated experience. We encourage you to apply if you meet the core qualifications, even if you don't match every item listed. Role Overview As an AI / ML Engineer at Nelnet, you'll be at the intersection of applied AI and software engineering. Your primary focus will be twofold: keeping our existing fleet of deployed agents healthy, evaluated, and improving over time, and designing new agentic capabilities that extend what those systems can do. Beyond building, you'll help expand the agentic approach across the organization, partnering with business stakeholders to identify high-value use cases, establishing reusable patterns and guardrails, and raising the bar for how teams evaluate and ship AI systems. You'll collaborate closely with Data Scientists, Product, Software Engineers, and business partners to move solutions from concept to production.

Requirements

Strong programming skills in Python.
Working knowledge of AWS services for AI workloads (e.g., Bedrock, Lambda, ECS/Fargate, S3, OpenSearch, Step Functions).
Familiarity with agent frameworks and orchestration tooling, and sound judgment about when a framework helps versus when to build directly.
Practical prompt and context engineering skill, with an evaluation-driven approach to improving them.
Experience with infrastructure as code (IaC) tools like Terraform.
Proficiency using CI/CD pipelines to automate testing and deployment of AI workflows.
Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
Solid grounding in machine learning fundamentals and statistical reasoning, sufficient to evaluate systems rigorously and know when a non-LLM approach is the better answer.
Excellent problem-solving skills and the ability to work in a collaborative environment.
Strong critical thinking, analytical, and quantitative problem-solving ability.
Strong organization, time management, and coordination skills to drive projects to completion.
Ability to communicate AI capabilities and limitations clearly to non-technical stakeholders.
Ability to lead end-to-end development of new products.
Bachelor’s or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field (or equivalent experience).
U.S. Citizenship AND the ability to obtain a U.S. 6C Security Clearance.
Minimum of 2 years of experience in machine learning engineering, AI engineering, software engineering, or related roles.
Demonstrated experience building agentic systems with large language models. Examples include tool calling, orchestration, multi-step workflows - not just single-turn prompting.
Hands-on experience evaluating LLM and agent systems, including designing eval sets and interpreting results to drive iteration.
Experience deploying and supporting AI or ML systems in production environments.
Experience with retrieval-augmented generation and other approaches to grounding models in enterprise data.

Responsibilities

Design, build, and deploy LLM-powered agents to solve concrete business problems. This includes tool use, multi-step reasoning, orchestration, and human-in-the-loop patterns.
Own the day-to-day health of agents already in production: monitor behavior, diagnose failures, tune prompts and tooling, and manage model and dependency upgrades without regressing quality.
Build and maintain evaluation suites for agent systems, including offline test sets, LLM-as-judge scoring, regression testing, and online metrics.
Instrument agents end to end, including traces, tool calls, token usage, latency, cost, and outcome quality and act on what the data shows.
Design retrieval and context strategies (RAG, structured data access, caching, chunking, ranking) that give agents the right information at the right time.
Build and maintain the tools, APIs, and connectors agents rely on, ensuring safe and reliable interaction with internal systems and data.
Design and implement scalable AI pipelines and services on AWS, using infrastructure as code (Terraform) and CI/CD to automate deployment and maintenance.
Implement safety controls, input/output validation, access boundaries, and audit trails appropriate to a regulated environment.
Partner with teams across the organization to identify where agents add real value, prototype quickly, and turn one-off wins into reusable frameworks and standards.
Maintain clear documentation of agent architectures, prompts, tool contracts, data flows, evaluation results, and known limitations.
Track the fast-moving foundation model and agent tooling landscape, and bring what's genuinely useful into our stack.
Provide guidance to team members on agent design, evaluation practices, and applied AI best practices.