Senior Applied & Agentic AI Engineer
Sedgwick
full-time
Posted on:
Location Type: Remote
Location: Idaho • Louisiana • United States
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Job Level
About the role
- 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.
- Experience operating in regulated industries such as insurance, financial services, or healthcare preferred.
- Proven leadership in technical design reviews, architecture governance, and cross-functional collaboration.
- Strong ability to balance innovation with enterprise risk management and operational stability.
Benefits
- Professional development opportunities
- Work-life balance
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
LLM systemsagentic AI systemsretrieval-augmented generation (RAG)multi-agent orchestrationworkflow automationPythonmicroservicesembedding strategiesvector databasesstructured output enforcement
Soft Skills
leadershipcross-functional collaborationinnovationrisk managementmentoringtechnical design reviewscommunicationproblem-solvingstrategic thinkingorganizational skills