Senior Applied & Agentic AI Engineer

Sedgwick is a leading claims management services company that values meaningful work and a supportive culture. They are seeking a Senior Applied & Agentic AI Engineer to lead the development of advanced AI systems that enhance operational workflows in claims and risk management.

Responsibilities:

• 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

Requirements:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or related discipline
• 7–10+ years of experience in AI engineering, machine learning systems, or distributed software architecture
• 3–5+ years designing and deploying LLM-powered systems in production environments
• Demonstrated experience architecting full agentic AI systems with planning, reflection, memory, and tool execution components
• Deep expertise in RAG architectures, embedding strategies, vector databases, and retrieval optimization
• Strong experience designing multi-agent orchestration frameworks and workflow engines
• Advanced proficiency in Python and enterprise API integration patterns
• Experience building secure, scalable microservices in cloud-native environments
• Strong understanding of distributed systems, event-driven architectures, and system reliability principles
• Experience implementing structured output enforcement, guardrails, and audit logging mechanisms
• Demonstrated ability to design evaluation and benchmarking frameworks for LLM and agent reliability
• Proven leadership in technical design reviews, architecture governance, and cross-functional collaboration
• Strong ability to balance innovation with enterprise risk management and operational stability
• Experience operating in regulated industries such as insurance, financial services, or healthcare preferred