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About This Role
Nelnet is a diversified and innovative company committed to enriching lives through the power of service as a student loan servicer, professional services company, consumer loan originator and servicer, payments processor, renewable energy solutions, and K\-12 and higher education expert. For over 40 years, Nelnet has been serving its customers, associates, and communities.
The perks of working at Nelnet go beyond our benefits package. When you join the Nelnet team, you're part of a community invested in the success of each individual. That support comes through in our work, as we are united by our mission of creating opportunities for people where they live, learn, and work.
The Cybersecurity AI Automation Engineer at Nelnet plays a critical role in advancing the organization's cybersecurity capabilities through intelligent automation and AI\-driven solutions. This position partners closely with cybersecurity and internal teams to design, build, and deploy automated workflows that increase operational efficiency, reduce manual effort, and surface actionable insights.
This role requires strong technical acumen, practical problem\-solving, and a collaborative mindset to deliver scalable solutions within Nelnet's AI infrastructure.
You will:
- Design and deploy AI\-powered automation pipelines that support operational workflows.
- Integrate automation solutions within Nelnet's existing AI infrastructure — LLM APIs, orchestration platforms, and workflow tooling.
- Consult with internal teams to eliminate manual, repetitive processes across cybersecurity operations through scalable AI solutions.
- Apply DevSecOps principles throughout the software delivery lifecycle.
- Collaborate with stakeholders to translate business and compliance requirements into well\-scoped automation solutions.
- Continuously evaluate emerging AI and automation capabilities for opportunities across cybersecurity team workflows.
AI Automation \& Solutions Engineering
- Design, develop, and deploy AI\-powered automation pipelines and tools that support cybersecurity and partner teams, with an emphasis on operational workflows and business processes.
- Build and integrate automation solutions within Nelnet's existing AI infrastructure, including LLM APIs, orchestration platforms, and workflow tooling, to ensure consistency, reuse, and long\-term maintainability.
- Consult with internal teams to eliminate manual, repetitive processes by engineering scalable AI solutions that drive measurable workflow efficiencies.
- Apply DevSecOps principles throughout the software delivery lifecycle, embedding security, testing, and quality practices into every phase of solution development and deployment.
- Collaborate with business stakeholders to translate business and compliance requirements into well\-scoped automation solutions, ensuring delivered tools align with risk program objectives and audit expectations.
- Continuously evaluate emerging AI and automation capabilities to identify opportunities for improvement across cybersecurity team workflows and tooling.
Innovation
- Bring a solutions\-engineering mindset to cybersecurity challenges, exploring practical and creative applications of AI beyond conventional approaches.
- Prototype and iterate on automation concepts quickly, validating feasibility in Nelnet's environment before scaling to production\-ready solutions.
- Stay current on developments in AI, LLM tooling, and automation frameworks, proactively introducing relevant advancements that could benefit cybersecurity operations.
Agility
- Operate effectively in a fast\-moving environment, adapting solutions as team needs, infrastructure capabilities, and security priorities evolve.
- Manage multiple concurrent automation initiatives, balancing stakeholder urgency with the rigor required for reliable, production\-grade delivery.
- Work across domains with strong engineering fundamentals, quickly building context in cybersecurity and business processes.
Problem Solving
- Analyze complex, manual cybersecurity workflows to identify root inefficiencies and design automation solutions that address them at the process level, not just the task level.
- Troubleshoot issues across the full automation stack, from data ingestion and model integration to pipeline execution and output delivery and resolve them efficiently.
- Approach ambiguous or novel use cases with structured thinking, breaking down large problems into actionable engineering work with clear outcomes.
Collaboration
- Partner closely with cybersecurity and stakeholder teams to understand workflow pain points and define automation requirements.
- Work alongside platform, infrastructure, and AI teams to ensure solutions are aligned with architectural standards and leverage shared capabilities.
- Act as a bridge between technical implementation and business stakeholders, translating engineering decisions into clear, accessible language.
- Mentor peers on AI automation capabilities and emerging tooling relevant to cybersecurity operations.
Communication Skills
- Communicate clearly with both technical and non\-technical audiences, adapting depth and framing to suit the audience.
- Document automation solutions thoroughly, including design decisions, integration dependencies, and operational runbooks to ensure maintainability.
- Present work in progress and completed solutions clearly, incorporating feedback and demonstrating responsiveness.
Strategic Focus
- Align automation efforts to Nelnet's broader cybersecurity strategy and AI infrastructure investments, ensuring solutions contribute to long\-term program goals.
- Prioritize high\-impact opportunities that reduce risk, improve consistency, and free up capacity for higher\-value work.
- Design solutions with scalability and reuse, enabling adoption across teams and use cases.
- Serve as a trusted advisor on AI automation and help shape how cybersecurity teams adopt and benefit from these capabilities.
EDUCATION:
Knowledge equivalent to the completion of a Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field of study.
EXPERIENCE:
- 2–4 years of experience in AI/ML engineering, automation development, solutions engineering, or DevOps/DevSecOps.
- Hands\-on experience building and integrating with LLMs or AI APIs (e.g., OpenAI, Anthropic, Azure OpenAI, or open\-source models).
- Proven experience developing automated workflows, data pipelines, or AI\-driven solutions in production environments.
- Experience with AI orchestration frameworks (e.g., LangChain, LlamaIndex, n8n, or similar) and prompt engineering practices.
- Experience with GRC or risk/compliance process automation is a plus.
- Security certifications (Sec\+, CISSP, etc.) not required but is a plus.
COMPETENCIES – SKILLS/KNOWLEDGE/ABILITIES:
Required:
- Proficiency in Python or a comparable language for building AI and automation solutions.
- Experience integrating LLMs and AI APIs into enterprise workflows and tooling.
- Experience building and maintaining automated pipelines, including data ingestion, transformation, and output delivery.
- Familiarity with DevSecOps principles and secure software development practices.
- Working knowledge of AI orchestration tools and the ability to evaluate and adopt new technologies as the landscape evolves.
- Strong communication skills, with the ability to translate business needs into technical solutions.
- Ability to work within existing AI infrastructure to identify inefficiencies and deliver scalable improvements.
- Excellent organizational, presentation, verbal, and written communication skills.
- Ability to assess and clearly communicate complexity, trade\-offs, and technical decisions to both engineering and management stakeholders.
- Demonstrated ability to stay current with emerging technologies and adapt to evolving business and technical environments.
Preferred:
- Experience with GRC platforms such as ServiceNow, Archer, or similar.
- Familiarity with cybersecurity domains (e.g., vulnerability management, compliance) or exposure to operational security workflows.
- Cloud infrastructure experience (AWS, Azure, or GCP), particularly in deploying or managing AI/ML services.
- Experience with compliance or business process automation use cases.
- Knowledge of cyber defense operations or security tooling ecosystems.
Please note that we are unable to provide visa sponsorship for this position. To be considered, candidates must already be authorized to work in the United States without the need for current or future sponsorship.
\*\*Pay Range for this role is \- $100,000 \- $135,000
Our benefits package includes medical, dental, vision, HSA and FSA, generous earned time off, 401K/student loan repayment, life insurance \& AD\&D insurance, employee assistance program, employee stock purchase program, tuition reimbursement, performance\-based incentive pay, short\- and long\-term disability, and a robust wellness program. Click here to learn more about our benefits: LINK.
Nelnet is committed to providing a welcoming and respectful workplace where all associates have the opportunity to succeed. As an Equal Opportunity Employer, we ensure that all qualified applicants are considered for employment. Employment decisions are made without regard to race, color, religion/creed, national origin, gender, sex, marital status, age, disability, use of a guide dog or service animal, sexual orientation, military/veteran status, or any other status protected by federal, state, or local law. We value the unique contributions of every team member and believe that a positive work environment benefits everyone.
Qualified individuals with disabilities who require reasonable accommodations in order to apply or compete for positions at Nelnet may request such accommodations by contacting Corporate Recruiting at 402\-486\-5725 or corporaterecruiting@nelnet.net.
Nelnet is a Drug Free and Tobacco Free Workplace.
Salary Context
This $100K-$135K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Nelnet, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($117K) sits 30% below the category median. Disclosed range: $100K to $135K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Nelnet AI Hiring
Nelnet has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $90K - $135K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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