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# Principal Software Engineer, AI

- **Company:** Pepper Auditors PLLC
- **Location:** Columbus, OH, United States (Remote available)
- **Salary:** $200,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Large Language Models, Front End Software Development, Virtual Agents
- **Published:** July 2, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=151aa12b978424de

## About the Role

* Have shipped production LLM systems where correctness mattered - extraction pipelines, agentic workflows, or document-understanding systems - ideally in a domain with real consequences (fintech, legal, healthcare, regtech).
 * Build evals as a reflex. You've maintained golden datasets and caught regressions before they reached users, and you treat "we can't measure whether this got better" as an unacceptable state.
 * Have strong judgment about where to use a model and where to write a deterministic function - with a hard bias toward the latter when dollars or compliance are on the line.
 * Are a genuine 0 1 builder - you can own a system end-to-end, ship it, and operate it in production. You're energized, not unsettled, by being the second engineer in the building.
 * Treat engineering hygiene as default, not overhead.

## Description

Pepper's mission is to protect the integrity of every American's employee benefit plan. We are a fast-growing team of technologists and CPAs working to build a modern audit firm that can serve as the fulfillment engine for federally mandated compliance work streams., We're looking for an experienced software engineer to help build and scale our audit workbench; a pipeline-based harness - not a generic agent loop - that takes an engagement from raw evidence to reviewable workpaper. As of today that looks roughly like:

 * Ingestion and extraction
 * Recomputation and exception detection
 * Workpaper generation
 * Eval infrastrcuture
 * Frontend review surface

As one of the first engineering hires, you will help set the technical direction to ensure our audit wokrbench is usable and scalable from our our first 100 clients to our next 10,000.

The hard part

Most "build an AI agent" roles optimize for demo velocity. This one optimizes for a number a CPA stakes their license on. The reliability of this system comes from schema design, deterministic rule logic, validation, and structured human review - not from hoping the model is good. You'll spend as much time on the deterministic-versus-stochastic boundary, eval harnesses, and traceability as on prompts and orchestration. The model does extraction and narrative drafting; the math and the regulatory logic are exact code. If "build something auditable, reproducible, and provably correct in a regulated domain" is more interesting to you than "ship a chatbot," you'll like it here.


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