AI FinOps Engineer

Nelnet•Remote - Nebraska, NE

9d•$77,000 - $170,000

About The Position

Nelnet is seeking an AI FinOps Engineer to own the token economics and cost optimization engine of our Enterprise AI program. Reporting to the IT Director of AI Delivery, this role is embedded in our Shared Services department and focused on driving efficiency across our Enterprise AI platforms — starting with Anthropic Claude and extending to the broader EA portfolio. This is a technical, hands-on role. You will work at the API level to instrument workloads, identify inefficiencies, and engineer solutions that reduce organizational cost without degrading capability. A key output of this work is translating token-level findings into best practices that our AI enablement team can distribute across the organization.

Requirements

1–2 years hands-on experience with LLM APIs (Claude, OpenAI, or equivalent) at the token level — not just usage, but optimization
Deep familiarity with LLM pricing mechanics: context windows, caching, batching, input/output token splits, and tier structures
Experience with prompt engineering techniques focused on efficiency and cost reduction
Python or SQL for instrumentation and pipeline work
Ability to communicate technical findings to non-technical stakeholders
United States citizenship (required due to government contract requirements)

Nice To Haves

2–4 years of industry experience
Prompt caching, batch API usage, or model-tier switching in production environments
Cloud FinOps background or FinOps Foundation certification
Experience with multiple LLM providers and their cost/capability tradeoffs

Responsibilities

Track, model, and optimize token costs across Enterprise AI platforms.
Own prompt efficiency patterns, caching strategies, and model-tier selection guidance.
Define and document token optimization best practices.
Partner with the AI enablement team to translate findings into org-wide guidance.
Build and maintain dashboards that surface usage trends, cost anomalies, and efficiency metrics for IT leadership.
Identify waste, propose tier or model changes, and quantify savings.
Own recommendations from analysis through implementation.