Job Description
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 Senior Engineer - LLMOps & MLOps Role Overview This is a high-stakes, execution-focused role within the Transformation Office. We are looking for a "day-one" engineer to own the production lifecycle of our AI initiatives. Your mission is to build the automated infrastructure that bridges our legacy data systems with modern AWS and Azure AI services. You will be responsible for the "Ops" of AI: ensuring that LLM applications, RAG pipelines, and traditional ML models are deployable, observable, and scalable in a multi-cloud environment. Key Responsibilities Multi-Cloud Pipeline Execution: Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio). LLMOps Framework Implementation: Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. Legacy Data Connectivity: Build the engineering "pipes" to securely ingest and move data from legacy systems (Mainframes, SQL Server, on-prem DBs) into cloud-native MLOps workflows. Automated Model Evaluation: Implement systemized frameworks for LLM evaluation (LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation to ensure performance before deployment. Observability & Monitoring: Deploy real-time monitoring