AI Engineer
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Job Description
AI Engineer owning production LLM workflows for Jeeves’ stablecoin-native banking platform. Building, evaluating, and monitoring AI systems for finance automation.
Responsibilities:
- Own finance workflows end to end, from problem definition through production and ongoing improvement
- Design agentic and LLM-based systems combining extraction, retrieval, reasoning, and tool use, with human-in-the-loop points for high-value financial decisions
- Write design documents, make build-vs-buy and model choices, and set workflow success metrics
- Engage directly with customers and internal finance users to understand workflow problems and define completion criteria
- Build production-grade LLM pipelines with prompt and context design, structured outputs, validation, fallbacks, and confidence scoring
- Design retrieval and RAG components including chunking, embeddings, vector search, and re-ranking
- Integrate AI services with Jeeves's backend using API contracts, retries, graceful degradation, and per-customer data isolation
- Manage model cost and latency
- Build evaluation sets and automated evaluations; detect regressions when prompts, models, or data change
- Instrument AI components with logging, tracing, dashboards, and alerts
- Maintain an audit trail of AI decisions for a regulated financial product
- Establish shared AI engineering patterns, tooling, and practices
- Demonstrate effective use of coding agents and AI tools to the wider team
- Review AI system designs and share learnings
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, 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 (for example Claude Code, Cursor, or Codex) as a regular part of how they 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
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