Lead AI Engineer
$170,000–$215,000 year
Remote
Job Summary
Define the AI engineering roadmap and architecture standards across the organization, leading build vs. buy vs. integrate decisions for AI systems and platforms. Architect enterprise AI solutions for the customer-facing digital insurance platform, establishing LLMOps practices including evaluation suites, cost observability, and production guardrails. Partner with Product and Engineering leadership to align capabilities with business outcomes, translating technical concepts for executives while championing AI-assisted coding practices and developer tooling. Serve as the ultimate technical escalation point for complex system design challenges, ensuring AI systems integrate cleanly with existing infrastructure. This systems leadership role operates within a fast-moving, independent insurance company serving small business owners nationwide. You will bring structure and discipline to the AI practice without direct people management responsibilities, focusing on high-stakes technical decisions and organizational standards.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field (or equivalent practical experience)
- 8+ years of software engineering experience, with demonstrated mastery designing and shipping production systems where correctness, reliability, and auditability matter
- 2+ years building production LLM/GenAI and agentic systems, plus fluency with AI-assisted coding tooling and the judgment to set org-wide standards, evals, and guardrails for AI-generated code
- Sound judgment about where AI belongs and where it must not – comfortable with probabilistic agents for customer-facing and research tasks, while keeping policy binding, money movement, and compliance flows deterministic, auditable, and human-governed
- Experience making and communicating build vs. buy vs. integrate decisions at an organizational level
- Proficiency in Python and the modern GenAI application stack – agent/orchestration frameworks (e.g. LangChain, LlamaIndex, or equivalents), model-provider SDKs, vector databases, and evaluation tooling
- Exceptional written and verbal communication skills – you can write a crisp architecture decision record and present it to a board-level audience
Desired Qualifications
- Experience in a technical lead or principal engineer role with broad organizational influence
- Background working closely with product engineering teams in a dual-track agile model
- Familiarity with RAG architectures, vector databases, and semantic search at scale
- Experience with AI cost management, observability, and governance frameworks
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