AIOps Engineer

Nelnet•Remote - Nebraska, NE

2d•$100,000 - $150,000

About The Position

Nelnet is seeking an AIOps Engineer to own the operational backbone of our Enterprise AI platforms and build the AI Agents that power our Shared Services teams. Reporting to the IT Director of AI Delivery, this role is embedded in Shared Services and focused on platform depth, agentic delivery, and cross-team enablement. You will start hands-on with Claude and Anthropic's tooling — prompt engineering, API integration, MCP server configuration, and the skills/evals ecosystem. The underlying principles — context engineering, tool-use patterns, Agent orchestration — transfer directly to custom Agent builds and other platforms that will fall under this team's ownership. Depth on one platform first, then breadth across the portfolio.

Requirements

1–2 years hands-on experience building with Claude or equivalent LLM APIs — prompt engineering, API integration, tool-use and function-calling patterns
Proficiency in Python and/or TypeScript/Node.js for AI application development
Familiarity with Agent orchestration concepts: multi-step reasoning, tool chaining, context management
Experience with MCP servers or equivalent integration patterns
Proficiency with context and token optimization
Familiarity with governance, compliance, and security principles as they apply to responsible AI deployments

Nice To Haves

2–4 years of industry experience
Experience with Copilot, Gemini, or other foundation models and workflow automation platforms
Familiarity with evaluation frameworks for AI output quality measurement
DevOps, CI/CD, or agile development background applied to AI deployment pipelines
Experience building autonomous AI Agents for enterprise workflows — not just chatbots or assistants

Responsibilities

Configure, maintain, and optimize Enterprise AI platform deployments (Claude, Copilot, Gemini) across Shared Services tenants.
Own authentication models and integration standards.
Build and deploy AI Agents for Shared Services teams using established pipeline standards — from platform-native builds to custom Agent development.
Build and maintain a shared resource repository of reusable components, patterns, and frameworks that inform enterprise standards.
Participate in recurring sessions to harvest segment innovations, standardize them, and redistribute org-wide.
Serve as the hands-on AI resource for segments without dedicated AI infrastructure.
Apply team development standards — branching strategy, review gates, CI/CD pipelines — to all AI solutions and platform work.