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# Senior AI Engineer

- **Company:** David Joseph & Company
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $170,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Large Language Models, Backend, Front End Software Development, Automation Anywhere
- **Published:** July 23, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2aa69a79312f9e31

## About the Role

* 2+ years building and shipping production AI agents
 * 4+ years backend or infrastructure engineering
 * Research background in ML, NLP, or AI agents
 * Early-stage startup experience (pre-seed to Series B)
 * NYC in-person, startup hours, 6 days a week, 9am to 7pm or 8pm

Green Flags

 * Strong agentic systems track record
 * Research background in ML, NLP, or agents
 * Graduated post-2021 with direct agentic-era experience
 * Fintech, ERP, or accounting software background

Red Flags

 * Frontend-heavy background with little backend depth
 * Generic AI resume without architectural depth
 * Multiple short tenures

## Description

Own a finance domain end-to-end - from context engineering and prompt design through tool use, evals, guardrails, and the UX around them - working directly with controllers, accountants, and CFOs to replace manual processes with production-grade agent systems. This is a backend-heavy, full-stack-in-practice role: no PM writes your specs and no architecture committee gates your ideas., * Own a finance domain end-to-end, building the agent system that automates it across context, prompts, tools, evals, guardrails, and UX

 * Design and implement durable, replay-safe orchestration for long-running AI workflows across flaky, stateful enterprise systems
 * Build the trust layer for non-deterministic systems: evals, verification, guardrails, and observability that catch agent mistakes before a human does
 * Ship full-stack when the work calls for it, owning decisions across the LLM pipeline, infrastructure, backend, and UX within your pod
 * Do serious context engineering: retrieval, memory, and tool design over large, messy, proprietary financial data
 * Build idempotent, audit-ready write-back systems for ERPs and financial systems with full traceability

Tech stack: Python (primary stack)


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