AI Engineer

The AI Engineer is part of AI Technology Platform and Solutions team within Enterprise Architecture. The role designs, builds, and hardens the AI systems, components, and patterns teams rely on, working closely with architects, engineers, and platform teams to take AI capabilities from prototype to production for underwriting, claims, distribution, and other business areas.

This is a hands-on engineering role that combines strong technical judgment with a builder’s focus on quality. The AI Engineer builds, evaluates, and operates AI systems in production in line with security, data, responsible AI, and operational-readiness expectations, delivering both supportable solutions and reusable components that help the whole team move faster.

Key Responsibilities

• Design, build, and operate agentic workflows, retrieval-augmented generation pipelines, and tool-calling systems using LangGraph, LangChain, and related frameworks, taking them from prototype to production.
• Build end-to-end AI solutions, including data models, integrations, services, model and agent components, and user-facing experiences needed to deliver business value.
• Apply the right technical approach for each problem, using agentic workflows, targeted model calls, or deterministic software based on value, risk, and fit.
• Build and maintain evaluation harnesses, test datasets, and regression suites for prompts, models, and agents, and define acceptance thresholds and production-readiness criteria before go-live, building for auditability, traceability, and responsible adoption.
• Instrument AI systems with tracing and monitoring for quality, token cost, and latency, and tune them for cost and performance in production.
• Build security, privacy, data governance, responsible AI, reliability, observability, and supportability into solutions from the start, partnering with control and platform teams to meet production requirements.

• 4+ years of professional software engineering experience, including 2+ years of hands-on experience building LLM-based systems in production, spanning prompt and context design, retrieval-augmented generation, tool use including Model Context Protocol (MCP), and agent orchestration with LangGraph and LangChain (or an equivalent framework such as Semantic Kernel or Microsoft Agent Framework).
• Production depth in stateful, multi-step agent workflows, including state management, human-in-the-loop checkpoints, error handling, and recovery.
• Experience building evaluation and observability for LLM systems, such as test datasets, automated and LLM-as-judge evaluations, and tracing with LangSmith or an equivalent tool.
• Strong Python skills and hands-on development experience across enterprise services and modern web applications. Experience with C#/.NET, TypeScript, and React or Angular is also relevant.
• Cloud-native architecture experience on Azure across compute, data, integration, identity, and AI services, including the tradeoffs among them.

Preferred Qualifications

• Event-driven architecture, microservices, API-first platform design, or data platform experience such as Databricks or Fabric.
• Insurance domain knowledge across underwriting, submission intake, policy administration, claims, or reinsurance, or delivery experience in another regulated industry such as healthcare or financial services where auditability was a first-class requirement.