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 Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio). Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization.

Build the engineering "pipes" to securely ingest and move data from legacy systems (Mainframes, SQL Server, on-prem DBs) into cloud-native MLOps workflows. Implement systemized frameworks for LLM evaluation (LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation to ensure performance before deployment. Deploy real-time monitoring for model drift, hallucination detection, latency, and token consumption to manage both quality and cost.

Manage all AI resources using Terraform or CloudFormation, ensuring the cloud posture is reproducible, secure, and follows a "Privacy by Design" mandate. Partner with teams using platforms like Palantir, Databricks, or Snowflake to ensure a high-fidelity data flow between analytical ontologies and production models. Work directly with central IT and Security to navigate IAM roles, VPC peering, and firewall configurations, clearing the path for rapid transformation.

Optimize model serving endpoints for high-throughput and low-latency, utilizing containerization (Docker/Kubernetes) and serverless architectures where appropriate. Establish rigorous version control for prompts (PromptOps), model weights, and data snapshots to ensure 100% auditability and rollback capability. Support the data science lifecycle by automating feature stores, feature engineering pipelines, and the transition of experimental notebooks into hardened production microservices.

Implement automated scanning and guardrails (e.g., Bedrock Guardrails or Azure Content Safety) to prevent prompt injection and data leakage. Qualifications Bachelor’s degree in Computer Science or a related field required; Master’s degree in a quantitative discipline highly desirable. 6+ years of engineering experience, with a minimum of 3 years strictly focused on MLOps or LLMOps in a production environment. Deep, hands‑on proficiency in both AWS and Azure ecosystems, including configuring Bedrock and Azure OpenAI services, private networking, and endpoint security.

Expert Python, SQL, and PySpark; extensive experience with containerization (Docker, Kubernetes) and orchestration tools (Airflow, Kubeflow, or Step Functions). Professional experience with evaluation and observability frameworks like LangSmith, Arize Phoenix, or WhyLabs. Strong understanding of statistical validation, model evaluation metrics, and the ability to partner with Data Scientists to optimize model performance.

Ability to move at the speed of a startup while maintaining collaborative relationships within a large‑scale enterprise IT landscape. 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. #J-18808-Ljbffr