Software Engineer, Applied AI

What We Do Yesterday's insurance wasn't built for today's risk. We see it in the data and we feel it in the field. Emerging technology can reinvent how risk is priced and managed, faster and smarter, anchored in proven expertise. First-movers will define the next era of commercial risk management, and Shepherd is building it. Shepherd is a technology-driven Managing General Underwriter (MGU) transforming commercial Property & Casualty insurance for high-hazard industries. Our mission is to make risk frictionless for the builders and operators shaping the physical world, protecting progress from concept through construction and into decades of operation. We're building the fastest, smartest commercial risk platform, where underwriting expertise, data, and automation work together to deliver: Faster decisions Smarter, more accurate pricing Better risk outcomes With Shepherd, safety, speed, and quality no longer trade off against one another. They compound. We're not just modernizing insurance products. We're building the risk infrastructure for the next generation of financial services, where technology, underwriting, and partnerships operate in harmony to support the world's most important industries and the progress they make possible. Our Investors In March 2026, Shepherd raised a $42M Series B — bringing total funding to over $60M — led by Intact Private Capital, the [... source excerpt omitted ...] systems that directly power our underwriting and pricing engine. This role sits at the intersection of product engineering, AI infrastructure, and real-world insurance workflows. You will design and ship production AI systems that meaningfully improve how we evaluate risk, price policies, and operate our business. What You’ll Do Partner with product, engineering, underwriting, and data teams to identify high-leverage AI opportunities Design, build, and deploy LLM-powered features across underwriting, pricing, and internal tooling Develop AI agents and retrieval-augmented systems that extract structured insights from unstructured insurance data Build production-grade AI infr [... source excerpt omitted ...] ve faster with higher quality decisions Establish best practices for prompt engineering, evaluation frameworks, and model reliability Own AI features end-to-end from ideation to production What We’re Looking For Strong backend engineering experience building scalable APIs and distributed systems Hands-on experience shipping AI-powered products into production Familiarity with LLMs, RAG architectures, embeddings, and vector databases Experience integrating AI systems into user-facing or workflow-driven applications Comfort with cloud infrastructure and deploying AI workloads at scale A product mindset and the ability to translate ambiguous business problems into concrete AI