AI Engineer

Jeeves is the stablecoin-native banking platform for global enterprises. Thousands of companies across 35 countries run their corporate cards, payments, treasury and spend on one platform, with more than $5B in annualized volume, payouts to 190 countries, and one of the fastest-growing stablecoin payment networks in the world.

As an AI Engineer at Jeeves, you will own whole workflows from end to end, not individual features. You will design agentic and LLM-based systems that combine extraction, retrieval, reasoning, and tool use to complete multi-step work, with clear human-in-the-loop points for high-value financial decisions.

Responsibilities:

• Own AI Workflows End to End: Take a finance workflow (for example invoice-to-payment, expense review, or transaction reconciliation) and own it from problem definition through production and ongoing improvement.
• Build Reliable AI in Production: Build production-grade LLM pipelines: prompt and context design, structured outputs, validation, fallbacks, and confidence scoring.
• Measure, Monitor, and Improve: Build evaluation sets and automated evals for every workflow you own, and use them to catch regressions when prompts, models, or data change.
• Raise the Bar for AI Engineering at Jeeves: Set patterns, shared tooling, and practices that other engineers can build on.

Requirements:

• 7+ years of professional software engineering experience, including at least 2 years building and operating LLM or AI-powered systems in production.
• A track record of owning a large system or workflow end to end, from scoping and design through launch and iteration, with limited direction.
• Hands-on experience shipping LLM-powered applications with APIs such as Anthropic, OpenAI, or similar.
• Strong proficiency in Python, plus solid backend fundamentals: REST APIs, PostgreSQL or similar relational databases, async patterns, and a major cloud provider (AWS, GCP, or Azure).
• Uses AI coding agents and tools (for example Claude Code, Cursor, or Codex) as a regular part of how they build.
• Experience with observability for AI systems: logging, tracing, dashboards, and quality monitoring.
• Professional fluency in English, written and spoken.