AI Engineer Lead
$179,273–$286,837 year
On-siteIrvine, California, United States
Job Summary
Partner with investment professionals to translate ambiguous needs into clear technical specifications for production generative AI applications. Design, build, and operate retrieval-augmented generation pipelines, including parsing, ingestion, chunking, embeddings, and vector storage. Architect and implement agentic workflows that plan multi-step tasks within explicit, auditable boundaries, applying responsible-AI judgment and guardrails for safety. Practice eval-driven development by defining acceptance criteria, building evaluation harnesses, and measuring correctness, latency, and hallucination. Take end-to-end ownership from discovery through operational excellence, instrumenting systems with observability, cost tracking, and audit trails. Apply FinOps practices to optimize token and infrastructure spend while integrating solutions with enterprise data systems and MLOps tooling. Embed security, privacy, and compliance controls including IAM, encryption, and audit logging to meet regulatory requirements.
Required Qualifications
- Minimum 10+ years of professional software engineering experience
- Strong proficiency in Python (or a comparable modern language)
- Hands-on production experience building and shipping LLM-powered applications, including advanced prompt engineering, retrieval, agent development, and evaluation
- Demonstrated experience designing and building end-to-end RAG pipelines and integrating LLM solutions with real systems
- Strong understanding of system design, APIs, distributed-systems concepts, and cloud-native development, with a track record of owning production systems on solid architectural foundations
- A disciplined approach to evaluation and testing for non-deterministic systems — you build evals and guardrails as a first-class part of the work, not an afterthought
- Strong communication skills: you can lead technical discovery, write clearly, and convey technical concepts to mixed audiences while keeping a low ego and a collaborative approach
- High agency and comfort navigating the ambiguity of a large, regulated organization, with the judgment to make trade-offs between scope, speed, and quality
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
- Experience implementing security, privacy, and compliance controls in production systems — for example IAM, encryption, audit logging, and data-governance practices, ideally in a regulated environment
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