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Jeeves is a stablecoin-native banking platform serving global enterprises with corporate cards, payments, treasury, and spend management. The company processes over $5B in annualized volume, serves thousands of customers across 35 countries, and has raised over $400M from top-tier investors including Andreessen Horowitz, Y Combinator, and Coinbase Ventures.
As an AI Engineer, you will own complete finance workflows end-to-end, from problem definition through production and ongoing improvement. Rather than working on isolated features, you'll take ownership of entire processes—such as invoice-to-payment, expense review, or transaction reconciliation—and 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.
Key responsibilities include:
**Own AI Workflows End to End**: Take finance workflows and own them from scoping through production. Design agentic and LLM-based systems that handle multi-step work. Write design docs, make build-vs-buy and model choices, and set success metrics. Talk directly with customers and internal finance users to understand pain points and define "done."
**Build Reliable AI in Production**: Develop production-grade LLM pipelines with prompt and context design, structured outputs, validation, fallbacks, and confidence scoring. Design retrieval and RAG components (chunking, embeddings, vector search, re-ranking) where workflows need grounding in customer documents or policies. Integrate AI services cleanly with Jeeves's backend with clear API contracts, retries, graceful degradation, and per-customer data isolation. Manage cost and latency across models.
**Measure, Monitor, and Improve**: Build evaluation sets and automated evals for every workflow you own. Instrument AI components with logging, tracing, dashboards, and alerts. Keep audit trails of AI decisions that meet regulated financial product standards.
**Raise the Bar for AI Engineering**: Set patterns, shared tooling, and practices that other engineers can build on. Show the wider team how to use coding agents and AI tools well. Review peers' AI system designs and share learnings.
This is a senior, hands-on role for people who are AI-native in how they work. You use coding agents and AI tools every day to move faster and know where they fall short. It is not a research role and not for someone who wants a narrow, well-defined ticket queue. You'll work closely with Product, Backend Engineering, and customers' finance teams but won't wait for someone to hand you a spec.
**Requirements:**
- 7+ years of professional software engineering experience, including at least 2 years building and operating LLM or AI-powered systems in production
- 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, including structured outputs, error handling, and evaluation
- Experience designing agentic or multi-step AI workflows (tool use, orchestration, human review steps), or RAG systems with vector databases such as pgvector, Pinecone, or Weaviate
- 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 (e.g., Claude Code, Cursor, or Codex) as a regular part of how you build, and can explain where they help and where they don't
- Experience with observability for AI systems: logging, tracing, dashboards, and quality monitoring
- Professional fluency in English, written and spoken
**Preferred Qualifications:**
- Experience in fintech, financial services, or another regulated industry where AI decisions must be accurate and auditable
- Experience automating finance or back-office workflows such as accounts payable, expense management, reconciliation, or document processing
- Has led a project or small group of engineers technically, even without formal management responsibility
- Startup or scale-up experience building systems from scratch
- Spanish or Portuguese fluency
- Open-source contributions, technical writing, or talks on applied AI
