VP AI Engineering
Role details
Job location
Tech stack
Job description
· Define and execute the enterprise AI engineering strategy aligned to Sedgwick's claims, risk, and client service transformation goals.
· Lead the architecture, development, and deployment of applied AI and agentic AI solutions across global operations.
· Build and scale a high-performing AI engineering organization, including Applied AI Engineers, Agentic AI Engineers, ML Engineers, and AI Platform teams.
· Establish standards for LLM integration, retrieval-augmented generation (RAG), multi-agent orchestration, workflow automation, and model lifecycle management.
· Oversee the design of autonomous and semi-autonomous AI systems that support claims intake, coverage analysis, fraud detection, compliance review, and operational optimization.
· Drive enterprise architecture decisions for AI platforms, including model hosting, orchestration layers, vector databases, evaluation frameworks, and observability tooling.
· Ensure scalable, secure integration of AI systems with claims platforms, policy systems, document repositories, and enterprise data environments.
· Define and enforce engineering best practices for prompt engineering, tool use, memory design, guardrails, structured outputs, and deterministic validation.
· Establish governance frameworks for Responsible AI, explainability, auditability, and regulatory compliance.
· Partner with cybersecurity, legal, compliance, and data governance teams to mitigate AI-related operational and regulatory risks.
· Develop robust evaluation and benchmarking methodologies to measure reasoning quality, workflow completion rates, hallucination risk, and system reliability.
· Oversee AI production operations including performance monitoring, drift detection, cost management, and service reliability.
· Translate executive-level business priorities into scalable AI platform capabilities and delivery roadmaps.
· Collaborate with Claims Operations, IT, Digital, and Product teams to identify high-impact AI use cases and drive measurable ROI.
· Lead build-versus-buy decisions for AI tooling, foundation models, orchestration frameworks, and enterprise integrations.
· Manage vendor relationships related to AI platforms, cloud providers, and model providers.
· Drive adoption of AI solutions across adjusters, supervisors, and client-facing teams through strong partnership and change management alignment.
· Mentor engineering leaders and establish a strong culture of technical excellence, innovation, and operational discipline.
· Present AI strategy, progress, risks, and outcomes to executive leadership and board-level stakeholders.
· Develop long-term AI capability roadmaps that position Sedgwick as a technology leader in claims and risk management.
Requirements
· Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or related field; advanced degree preferred.
· 10+ years of experience in software engineering, AI engineering, or platform architecture.
· 5+ years of leadership experience managing high-performing technical teams.
· Demonstrated experience deploying LLM-powered systems and agentic AI solutions in enterprise environments.
· Deep expertise in RAG architectures, vector databases, orchestration frameworks, and workflow automation systems.
· Strong understanding of distributed systems, cloud-native architectures, and microservices design.
· Experience building secure integrations with enterprise systems and legacy platforms.
· Proven ability to design and implement AI governance, auditability, and Responsible AI frameworks.
· Experience operating in regulated industries such as insurance, healthcare, or financial services preferred.
· Strong financial and operational acumen with the ability to manage budgets and measure ROI.
· Ability to communicate complex AI concepts to non-technical executives and business stakeholders.
· Demonstrated track record of delivering large-scale, production AI systems with measurable business impact.
· Strong leadership presence with the ability to drive alignment across cross-functional enterprise teams.