Key skills
Large Language Models (LLM)Retrieval-Augmented Generation (RAG)Vector Search SystemsMulti-Agent SystemsWorkflow Orchestration EnginesPlanning LoopsContextual MemoryTool-Augmented LLM ReasoningPythonAPI-Driven System DesignEnterprise Platform IntegrationAzure OpenAIContainerized Service DeploymentCI/CD PipelinesDistributed SystemsMicroservicesEvent-Driven ArchitecturesGuardrails ImplementationAccess ControlsAuditability MechanismsLLM Evaluation MethodologiesInsurance DomainClaims DomainHealthcare DomainAIArtificial IntelligenceLLMOpenAIRAGAgenticData EngineeringAnalyticsAzureCRMCI/CDLeadership
About this role
Sedgwick is a company dedicated to helping people facing unexpected challenges, and they are seeking an Applied & Agentic AI Engineer to join their team. The role involves architecting and deploying AI solutions to enhance claims processes, including fraud detection and policy interpretation, while collaborating with various teams to integrate AI capabilities into workflows.
Responsibilities:
- Architect and deploy LLM-powered and agentic AI solutions that transform claims intake, policy interpretation, fraud detection, and resolution workflows
- Design end-to-end retrieval-augmented generation (RAG) systems leveraging enterprise knowledge bases, policy documents, SOPs, and historical claims data
- Build autonomous and semi-autonomous agents capable of reasoning, planning, and executing multi-step claims processes
- Develop stateful workflow orchestration layers that manage context, memory, and task sequencing across interactions
- Implement planning and reflection loops that decompose complex claims scenarios into structured subtasks
- Enable dynamic tool use through function calling and secure API integrations with claims systems, CRM platforms, document repositories, and analytics tools
- Develop document intelligence pipelines using LLMs for summarization, entity extraction, classification, validation, and timeline reconstruction
- Design structured prompt frameworks that enforce deterministic outputs and domain-aware reasoning
- Build multi-agent systems that coordinate document review, coverage analysis, compliance checks, and decision support
- Implement human-in-the-loop checkpoints for escalation, review, and override of AI-driven decisions
- Develop guardrails, output validation layers, and hallucination mitigation strategies
- Enforce structured outputs using schemas, type validation, and deterministic post-processing logic
- Optimize token consumption, inference latency, and cloud infrastructure costs
- Deploy scalable AI microservices using containerization and cloud-native architectures
- Implement monitoring for model drift, retrieval quality degradation, reasoning failures, and workflow breakdowns
- Maintain detailed audit logs of model decisions, agent reasoning steps, and tool executions
- Develop evaluation frameworks to test reasoning accuracy, workflow completion rates, and system reliability
- Collaborate with data engineering to build embedding pipelines, feature stores, and vector indexing strategies
- Ensure compliance with Responsible AI standards, data privacy regulations, and enterprise governance policies
- Partner with claims operations leadership to embed AI capabilities directly into adjuster and supervisor workflows
- Measure business impact through cycle-time reduction, automation coverage, fraud detection lift, and operational efficiency gains
Requirements:
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or related field
- 5+ years of experience building production-grade AI or advanced software systems
- 2–4+ years of hands-on experience with LLM-powered applications and orchestration layers
- Strong expertise in retrieval-augmented generation architectures and vector search systems
- Experience designing and implementing multi-agent systems and workflow orchestration engines
- Deep understanding of planning loops, contextual memory, and tool-augmented LLM reasoning
- Strong proficiency in Python and API-driven system design
- Experience integrating enterprise platforms and building secure connectors
- Familiarity with Azure OpenAI or similar enterprise LLM environments
- Experience deploying containerized services and managing CI/CD pipelines
- Understanding of distributed systems, microservices, and event-driven architectures
- Experience implementing guardrails, access controls, and auditability mechanisms
- Strong knowledge of evaluation methodologies for LLM reliability and agent performance
- Ability to translate complex operational workflows into scalable, AI-driven autonomous systems
- Experience in insurance, claims, healthcare, or other regulated industries preferred