Applied & Agentic AI Engineer

Sedgwick

About The Position

By joining Sedgwick, you'll be part of something truly meaningful. It’s what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there’s no limit to what you can achieve. Newsweek Recognizes Sedgwick as America’s Greatest Workplaces National Top Companies Certified as a Great Place to Work® Fortune Best Workplaces in Financial Services & Insurance Applied & Agentic AI Engineer Job 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. Qualifications 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. Experience in insurance, claims, healthcare, or other regulated industries preferred. Ability to translate complex operational workflows into scalable, AI-driven autonomous systems. Sedgwick is an Equal Opportunity Employer and a Drug-Free Workplace. If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles. Sedgwick is the world’s leading risk and claims administration partner, which helps clients thrive by navigating the unexpected. The company’s expertise, combined with the most advanced AI-enabled technology available, sets the standard for solutions in claims administration, loss adjusting, benefits administration, and product recall. With over 33,000 colleagues and 10,000 clients across 80 countries, Sedgwick provides unmatched perspective, caring that counts, and solutions for the rapidly changing and complex risk landscape. For more, see sedgwick.com

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.

Nice To Haves

Experience in insurance, claims, healthcare, or other regulated industries preferred.

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.