AI Engineer

• 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

• 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