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# AI / ML Engineer

- **Company:** Nelnet
- **Location:** Englewood, CO, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $95,000.0 - $130,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Application Frameworks, Audit Trail, Automation of Tests, Computer Programming, Continuous Integration, Data Security, Python (Programming Language), Machine Learning, Regression Testing, Software Engineering, Data Streaming, Systems Integration, Workflow Management Systems, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Containerization, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, Deployment Automation, AWS Fargate, Machine Learning Operations, Terraform, Automation Anywhere, Docker
- **Published:** August 8, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3347471000&tx=YT707CTZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

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., 1. Strong programming skills in Python.

 2. Working knowledge of AWS services for AI workloads (e.g., Bedrock, Lambda, ECS/Fargate, S3, OpenSearch, Step Functions).
 3. Familiarity with agent frameworks and orchestration tooling, and sound judgment about when a framework helps versus when to build directly.
 4. Practical prompt and context engineering skill, with an evaluation-driven approach to improving them.
 5. Experience with infrastructure as code (IaC) tools like Terraform.
 6. Proficiency using CI/CD pipelines to automate testing and deployment of AI workflows.
 7. Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
 8. 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.
 9. Excellent problem-solving skills and the ability to work in a collaborative environment.
 10. Strong critical thinking, analytical, and quantitative problem-solving ability.
 11. Strong organization, time management, and coordination skills to drive projects to completion.
 12. Ability to communicate AI capabilities and limitations clearly to non-technical stakeholders.
 13. 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.

## Description

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., 1. Agent Development : 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.

 2. Operating the Existing Fleet : 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.
 3. Evaluation : Build and maintain evaluation suites for agent systems, including offline test sets, LLM-as-judge scoring, regression testing, and online metrics.
 4. Observability and Monitoring : Instrument agents end to end, including traces, tool calls, token usage, latency, cost, and outcome quality and act on what the data shows.
 5. Context and Retrieval Engineering : Design retrieval and context strategies (RAG, structured data access, caching, chunking, ranking) that give agents the right information at the right time.
 6. Tooling and Integrations : Build and maintain the tools, APIs, and connectors agents rely on, ensuring safe and reliable interaction with internal systems and data.
 7. Scalability and Infrastructure : Design and implement scalable AI pipelines and services on AWS, using infrastructure as code (Terraform) and CI/CD to automate deployment and maintenance.
 8. Guardrails and Responsible AI : Implement safety controls, input/output validation, access boundaries, and audit trails appropriate to a regulated environment.
 9. Expanding Agentic Adoption : 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.
 10. Documentation : Maintain clear documentation of agent architectures, prompts, tool contracts, data flows, evaluation results, and known limitations.
 11. Innovation : Track the fast-moving foundation model and agent tooling landscape, and bring what's genuinely useful into our stack.
 12. Mentorship : Provide guidance to team members on agent design, evaluation practices, and applied AI best practices.


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