AI engineer for banking workflows

This company builds and licenses AI agents that automate critical customer lifecycle workflows for major financial institutions. They run real agentic systems in production, tackling long, high-stakes banking processes. Backed by leading VCs, they're scaling rapidly with significant traction. This is not a siloed research role. You will own the infrastructure that keeps these production agents reliable: the evals, feedback loops, prompt engineering, and fallbacks.

• Design and implement agent reliability infrastructure, including evals, feedback loops, prompt engineering, and fallbacks.
• Build and maintain human-in-the-loop decision logic for high-stakes agent actions.
• Take ownership of proprietary supervised and self-supervised ML models, applying recent academic work pragmatically.
• Contribute across the full agentic and software stack, including Node and TypeScript.
• Shape the evolving data strategy for production AI agents in a rapidly growing environment.

• Production experience building and maintaining agentic or LLM infrastructure: evals, feedback loops, prompt engineering, fallbacks, and human-in-the-loop decision logic.
• A pragmatic ML foundation across supervised and self-supervised methods.
• Product-leaning and full-stack in mindset; comfortable contributing to a Node and TypeScript software stack.
• Strong system design capabilities, with proven adaptability in a technical assessment (e.g., TypeScript).
• Thrives in a fast-paced startup environment, executing at pace and owning your lane while data strategy evolves.

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