AI SecOps Engineer

Nelnet•Remote - Nebraska, NE

2d•$100,000 - $150,000

About The Position

Nelnet is seeking an AI SecOps Engineer to own the security and compliance posture of our Enterprise AI program. Reporting to the IT Director of AI Delivery, this role is the technical bridge between AI governance policy and platform implementation — embedded in Shared Services and partnered closely with our Cyber Security Group (CSG). This is not a policy role. You will be hands on keyboard, building and developing solutions directly — defining architecture standards, translating compliance requirements into engineering guardrails, and making sure secure, responsible AI is baked in from the start — not bolted on at the end. You will start with Claude and scale to the full EA portfolio and custom Agent builds as the enterprise grows.

Requirements

1–2 years hands-on experience applying security and compliance controls to AI or ML systems
Familiarity with LLM-specific risks: prompt injection, data leakage, model access control, output filtering
Experience defining architecture standards or technical guardrails
Familiarity with data residency requirements, PII handling, and access governance in enterprise environments
Ability to translate security requirements into developer-facing guidance
Demonstrated ability to build and implement solutions directly, not just document or advise

Nice To Haves

2–4 years of industry experience
Familiarity with Anthropic's enterprise security model and data residency options
Cloud security background (AWS/Azure) applied to AI workloads
Experience with SOC I/II, FedRAMP, ISO 42001/42005, or NIST AI RMF
Experience working in SIEM or log aggregation platforms (e.g., Sentinel, Splunk, Google SecOps) to investigate AI-related signals and anomalies
Relevant certifications: CISSP, CCSP, or AI-specific security credentials

Responsibilities

Own the working relationship with CSG on data residency, PII handling, access governance, and model security controls.
Translate policy into guardrails the delivery team and citizen developers can act on.
Build and maintain security tooling, guardrail enforcement, and policy-as-code integrations across Enterprise AI platforms.
Reduce manual review through automation where possible.
Develop reusable security components and patterns that delivery teams and citizen developers can drop into Agent builds — making the secure path the easy path.
Instrument AI platforms to detect anomalous behavior, access patterns, and policy violations.
Build the detection layer, not just consume it.