Remote Senior AI Engineer — Agentic & LLM Orchestration

Sedgwick; Flagstaff (AZ); On-site; USD 150,000 - 190,000; Full time

Job summary

Sedgwick is seeking a Senior and Applied/Agentic AI Engineer to lead architecture and delivery of enterprise-grade LLM and agentic AI systems that transform claims, risk, and operational workflows. You will define technical strategy for RAG, multi-agent orchestration, and tool execution, while ensuring secure, scalable integration across platforms.

The role requires deep expertise in embedding strategies, vector databases, and evaluation frameworks, with leadership in design reviews and

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Engineering, or related discipline.
• 7–10+ years of experience in AI engineering, ML systems, or distributed software architecture.
• 3–5+ years designing and deploying LLM-powered systems in production.

Responsibilities

• Lead architecture and delivery of enterprise-grade LLM and agentic AI systems for claims, risk, and workflows.
• Define technical strategy for RAG, multi-agent orchestration, and autonomous workflow automation.
• Design and implement memory-aware agentic systems with planning, tool execution, reflection, and recovery.
• Architect stateful, memory-aware AI systems for long-running claims processes across touchpoints.
• Build multi-agent collaboration models for coverage analysis, document validation, fraud signals, and compliance checks.

Skills

Python

LLM systems

RAG architectures

Vector databases

Agent orchestration

Cloud-native

Microservices

Distributed systems

API integration

Planning & memory

Leadership

Education

Bachelor's/Master's in CS

Tools

Vector databases

LangChain

PyTorch

Kubernetes

Job description

Sedgwick is seeking a Senior and Applied/Agentic AI Engineer to lead architecture and delivery of enterprise-grade LLM and agentic AI systems that transform claims, risk, and operational workflows. You will define technical strategy for RAG, multi-agent orchestration, and tool execution, while ensuring secure, scalable integration across platforms.

The role requires deep expertise in embedding strategies, vector databases, and evaluation frameworks, with leadership in design reviews and