C

Crum & Forster

Principal AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • A bachelor's degree or equivalent in computer science, engineering, data science, or a related field.
  • At least 10 years of experience in software engineering, solution architecture, platform engineering, or AI/ML development.
  • At least 4 years of hands-on experience designing and delivering Generative AI, AI-Native, or LLM-based solutions in production environments.
  • Demonstrated experience building agentic AI systems in production, including orchestration, tool calling, retrieval frameworks, reasoning workflows, and autonomous task execution.
  • A track record of defining technical standards or solution patterns adopted by engineering teams beyond the candidate's own team.
  • Experience leading design reviews or serving as a technical authority across multiple teams without relying on direct reporting lines.
  • Strong software engineering skills, including expertise in Python and modern cloud-native application development.
  • Hands-on experience with AWS and modern AI services such as Amazon Bedrock.
  • Experience developing and integrating APIs, enterprise platforms, and distributed systems.
  • Strong understanding of cloud architecture, security, governance, ADLC delivery, and operational excellence.
  • Demonstrated ability to lead complex technical initiatives and influence architectural decisions across organizational boundaries.

Responsibilities

  • Lead architecture, design, and implementation of enterprise-scale Generative AI and agentic AI solutions, including target-state design for foundation model integration, retrieval-augmented generation, tool use, orchestration frameworks, and multi-agent patterns.
  • Define a reusable AI capability and pattern library for federated teams to build on, ensuring capabilities are built once and inherited rather than rebuilt.
  • Lead AI solution design reviews across central and federated builds and provide technical sign-off within AI readiness gates.
  • Set technical standards for evaluation, groundedness, guardrails, observability, security, and cost engineering, and hold builds to those standards.
  • Evaluate AI platforms and tooling and recommend direction aligned with Enterprise Architecture standards.
  • Personally design, build, and deliver foundational shared AI capabilities, including platform, runtime, retrieval primitives, agent frameworks, and the evaluation harness.
  • Lead technical delivery of prioritized flagship initiatives that establish patterns for subsequent work.
  • Build AI-powered assistants, copilots, workflow automation, and knowledge retrieval systems to improve business outcomes and employee productivity.
  • Use AWS Bedrock, Bedrock AgentCore, MCP, and related technologies to build scalable, secure, and maintainable AI applications.
  • Establish and enforce engineering practices for AI evaluation, model risk, observability, auditability, and responsible AI in partnership with the AI Risk and Reliability Engineering Lead.
  • Ensure every AI solution is instrumented for groundedness, quality, drift, and cost from the first commit rather than retrofitted before go-live.
  • Provide technical leadership and mentorship to AI engineers on the central team and engineers building AI in federated development domains.
  • Lead the technical Community of Practice, including shared libraries, office hours, code and design reviews, and internal enablement on agentic engineering.
  • Partner with external accelerators to absorb expertise into the ADLC practice, mentor engineers, and provide technical leadership across AI platform and application development efforts.
  • Stay current on emerging AI technologies and recommend approaches that create business value.

Desired Qualifications

  • Experience within Property & Casualty insurance or another highly regulated industry.
  • Experience with Bedrock AgentCore, MCP, vector databases, knowledge retrieval platforms, and AI evaluation frameworks.
  • Experience establishing an AI evaluation practice, including golden sets, groundedness and citation scoring, and regression testing of model-based systems.
  • Familiarity with AI observability, governance, model risk management, and responsible AI practices.
  • Experience with FinOps or cost engineering for large language model workloads.
  • Prior experience as a Principal Engineer, Distinguished Engineer, Staff Engineer, or equivalent senior individual-contributor technical leadership role.

About the Company

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