> Markdown version of [/jobs/ext/4214090-staff-software-engineer-ai](https://www.wearedevelopers.com/jobs/ext/4214090-staff-software-engineer-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Staff+ Software Engineer, AI

- **Company:** Aleph America Corporation
- **Location:** United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Spreadsheets, Microsoft Software, Software Engineering, Large Language Models, Prompt Engineering, Agentic-AI
- **Published:** October 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/staff-software-engineer-ai--04ccb438-4772-44ad-9506-18f8f5614a72

## About the Role

Software Engineering, Artificial Intelligence (AI), Aleph, Artificial Intelligence (AI), Finance, Spreadsheets, Financial Planning and Analysis (FP&A), Problem Solving Skills, Category Development, Microsoft Product Family, * You've shipped production LLM systems - evals, context management, multi-model or multi-provider routing - and can talk concretely about what broke and what you changed

 * You build proof of concepts, decide quickly, and ship v0s. Evals drive your quality bar
 * Strong judgment about abstractions: opinionated about design, pragmatic about shipping incrementally
 * Experience with LLM APIs and agent frameworks, and shipped user-facing products
 * You want to ship production systems, not do research

## Description

* Own the AI foundation product teams build on: model selection and routing, the model proxy layer, context management, and tool design
 * Own eval infrastructure. Work with customers and finance domain experts to define what excellent output looks like, then encode it in evals every team can run - in FP&A, a wrong answer ends up in someone's board deck
 * Ship agentic features end to end, from v0 through the optimization loop: prompt and context engineering, caching, parallel tool calls, subagent patterns
 * Build observability into agent behavior so we can profile it, find bottlenecks, and decide what to fix with data
 * Track what's changing in agentic systems and bring the practices that prove out into how we build