AI Engineer

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Lead the architecture and delivery of enterprise-grade LLM and agentic AI systems that transform claims, risk, and operational workflows.
• Define technical strategy for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation.
• Design and implement advanced agentic systems capable of planning, reasoning, tool selection, execution, reflection, and recovery.
• Architect stateful, memory-aware AI systems that manage long-running claims processes across multiple touchpoints.
• Build multi-agent collaboration models that coordinate coverage analysis, document validation, fraud signals, compliance checks, and decision support.
• Establish orchestration frameworks that manage task routing, context persistence, structured outputs, and failure handling.
• Design secure tool integration layers connecting agents to claims systems, policy platforms, data warehouses, document repositories, and external data services.
• Implement deterministic guardrails, schema validation, and output verification pipelines to reduce hallucination and execution risk.
• Lead development of document intelligence systems leveraging LLMs for summarization, entity extraction, discrepancy detection, and structured data reconstruction.
• Define prompt engineering standards and reusable reasoning templates for consistent, domain-aware outputs.
• Oversee embedding strategies, vector indexing architecture, retrieval optimization, and knowledge grounding approaches.
• Design evaluation frameworks to measure reasoning depth, workflow completion accuracy, hallucination rates, latency, and cost efficiency.
• Implement observability layers that track agent decisions, tool usage, retrieval effectiveness, and drift across models and prompts.
• Drive optimization strategies for token efficiency, caching, batching, and inference scaling.
• Ensure compliance with Responsible AI principles, enterprise governance standards, audit requirements, and regulatory constraints.
• Partner with enterprise architecture, cybersecurity, and data governance teams to define secure deployment patterns.
• Mentor engineers on LLM orchestration patterns, workflow decomposition, and safe agent design.
• Translate executive-level business objectives into scalable AI platform capabilities.
• Lead proof-of-concepts through full production deployment with measurable ROI outcomes.
• Continuously evaluate emerging foundation models, orchestration frameworks, and agent tooling for enterprise readiness.

Senior Applied AI Engineer

• Designing, developing, and deploying generative AI models (e.g., LLMs, diffusion models)
• Architecting and building agentic systems with autonomous decision-making capabilities
• Integrating generative AI and agentic solutions into existing products and services
• Developing and maintaining the software infrastructure required for AI/ML pipeline
• Developing and implementing data pipelines for training and evaluating AI models
• Conducting research and experimentation with new generative AI and agentic techniques
• Other duties as assigned or requested

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Design the entire ecosystem for AI solutions
• Define end-to-end architecture for AI systems, ensuring scalability and integration
• Establish best practices for MLOps
• Lead cross-functional collaboration with Data Science, DevOps, and Product teams
• Mentor senior engineers and present architectural decisions to executive leadership

Lead AI Architect

• Own the end-to-end administration of the Association’s primary AI platform and Microsoft Copilot
• Manage user provisioning and deprovisioning, SSO configuration, permission tiers, and seat allocation across standard and Pro/Max license tiers
• Ensure platform configurations meet enterprise security standards and Responsible AI guidelines
• Design, build, and maintain integrations between AI platforms and core enterprise systems
• Partner with Enterprise Risk to ensure all AI platform configurations meet data security policies
• Track platform consumption patterns and generate usage analytics
• Serve as the primary technical point of contact with AI platform vendors
• Lead technical due diligence when new AI tools are proposed
• Own the build and maintenance of internal CoE tooling
• Forecast platform capacity needs as adoption grows

Director of AI Platform Operations

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• 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.

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