AI Engineer

What You'll Do:

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.
• 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.
• Write the design docs, make the build-vs-buy and model choices, and set the success metrics for each workflow you own.
• Talk directly with customers and internal finance users to understand where the workflow breaks today and what "done" looks like.

Build Reliable AI in Production

• Build production-grade LLM pipelines: prompt and context design, structured outputs, validation, fallbacks, and confidence scoring.
• Design retrieval and RAG components (chunking, embeddings, vector search, re-ranking) where a workflow needs grounding in customer documents or policies.
• Integrate AI services cleanly with Jeeves's backend: clear API contracts, retries, graceful degradation, and per-customer data isolation.
• Manage cost and latency across models, choosing the right model for each step.

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.
• Instrument AI components with logging, tracing, dashboards, and alerts so you know when quality drops before a customer does.
• Keep an audit trail of AI decisions that meets the standards of a regulated financial product.

Raise the Bar for AI Engineering at Jeeves

• 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 in day-to-day engineering.
• Review peers' AI system designs and share what you learn openly.

Minimum 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.

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.