Lead AI Engineer

InsCipher•Pleasant Grove, UT

3d•$200,000 - $220,000

About The Position

We are seeking a Lead AI Engineer to serve as our organization's foremost technical authority on artificial intelligence strategy, architecture, and adoption. At InsCipher, that means shaping how AI is built and scaled across a compliance-critical platform – one that serves surplus lines brokers, state departments of insurance, and associations nationwide through tax filing, state reporting, and regulatory workflow automation. This is not a people management role – it is a systems leadership role. You will act as the connective tissue between engineering, product, and business leadership, making and owning the high-stakes technical decisions: build vs. buy, platform selection, architectural patterns, and AI tooling standards. You will bring structure and discipline to our AI practice while operating with the urgency and adaptability of a fast-moving, mission-driven company.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience
5+ years of software engineering experience with a meaningful portion in production AI/ML or LLM/GenAI systems – strong judgment and vision at the 5–6 year mark will not be screened out on tenure alone
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
Demonstrated track record of taking an existing business process apart and rebuilding it with AI at the core within an existing enterprise environment – shipped to production and owned the outcome
Production experience with LLM/GenAI and agentic systems – agent orchestration, tool calling, RAG, structured outputs – with the fluency 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 treating policy binding, money movement, and compliance flows as deterministic, auditable, and human-governed; this framing is non-negotiable in an insurance context
Experience making and communicating build vs. buy vs. integrate decisions at an organizational level
Proficiency in Python and the modern GenAI application stack – agent and orchestration frameworks, model-provider SDKs, vector databases, and evaluation tooling; screened for fluency with the stack generally, not a specific checklist of tools
Exceptional written and verbal communication skills – you can write a crisp architecture decision record and present it to a board-level audience

Nice To Haves

Direct evidence of legacy-process redesign with real latitude to alter underlying platform architecture – not just process work layered on top of stable infrastructure
Enterprise or regulated-domain experience, ideally in a business context closer to insurance or financial services
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
Experience with AI cost management, observability, and governance frameworks

Responsibilities

Define the AI engineering roadmap and architecture standards across the organization
Lead build vs. buy vs. integrate decision-making for AI systems and platforms – and be accountable for articulating how you arrived at those decisions
Evaluate emerging tools, frameworks, and models; provide clear, defensible recommendations to leadership
Serve as the ultimate technical escalation point for complex AI/ML system design challenges
Architect AI solutions for our enterprise platform – which is actively being upgraded and requires someone comfortable working across both the process layer and the underlying platform architecture as a normal part of the role, not just layering AI on top of stable infrastructure
Design systems for scale, reliability, and cost-efficiency in production environments
Establish LLMOps practices – evaluation and regression suites for LLM-powered features, cost and quality observability, versioned prompts and configs with staged rollout and rollback, and production guardrails
Ensure AI systems integrate cleanly with existing product and engineering infrastructure
Partner closely with Product and Engineering leadership to align AI capabilities with business outcomes
Receive operational support to help initiate and coordinate cross-functional engagement, allowing you to stay focused on technical leadership and decision-making
Provide clear guidance on resource allocation – who owns what, who should be engaged, and in what sequence
Translate complex technical concepts for non-technical audiences, including executives and business stakeholders
Challenge direction constructively and push back on approaches that compromise long-term system health
Champion and introduce AI-assisted coding practices and developer tooling across the engineering organization
Define standards for responsible AI development including guardrails, evaluation frameworks, and security considerations
Drive continuous improvement in how the organization builds and ships AI-powered features
Required to perform other duties as requested, directed, or assigned