Senior AI Engineer
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Job Description
Senior AI Engineer building and operating production LLM workflows for Jeeves' stablecoin-native banking platform. Owning AI systems end to end across design, deployment, evaluation, and monitoring.
Responsibilities:
- Own AI workflows end to end, including design, shipping, measurement, and continuous improvement
- Design agentic and LLM-based systems combining extraction, retrieval, reasoning, and tool use, with human-in-the-loop points
- Write design documents, make build-vs-buy and model choices, and set workflow success metrics
- Build production-grade LLM pipelines with prompt and context design, structured outputs, validation, fallbacks, and confidence scoring
- Integrate AI services with Jeeves's backend using clear API contracts, retries, graceful degradation, and per-customer data isolation
- Manage cost and latency across models
- Build evaluation sets and automated evaluations to catch regressions
- 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
- Teach the wider team to use coding agents and AI tools effectively
- Review peers' 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)
- 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)
- Experience with observability for AI systems: logging, tracing, dashboards, and quality monitoring
- Professional fluency in English, written and spoken
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