Senior Engineer - LLMOps & MLOps

Sedgwick; Santa Fe (NM); On-site; USD 100,000 - 130,000; Full time

Job summary

A leading AI innovation company in Santa Fe, New Mexico is seeking an experienced engineer to manage the production lifecycle of AI initiatives. This role focuses on building automated infrastructure that connects legacy systems with modern AI services on AWS and Azure. Candidates should possess 6+ years of engineering experience, especially in MLOps, with strong skills in Python, SQL, and Azure services. This is a critical role for ensuring the scalability and observability of AI applications.

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, minimum of 3 years focused on MLOps or LLMOps in production.
• Deep proficiency in AWS and Azure ecosystems, including configuring services and networking.
• Expert in Python, SQL, PySpark; experience with Docker, Kubernetes, and orchestration tools.
• Experience with evaluation and observability frameworks like LangSmith or WhyLabs.

Responsibilities

• Build and maintain CI/CD and CT pipelines across AWS and Azure.
• Design infrastructure for Retrieval-Augmented Generation (RAG) and optimize database management.
• Build pipelines to ingest and move data into cloud-native MLOps workflows.
• Deploy monitoring for model drift and manage quality and cost.
• Partner with teams to ensure high-fidelity data flow between analytics and production.

Skills

MLOps

AWS

Azure

Python

SQL

PySpark

Docker

Kubernetes

Airflow

Statistical validation

Education

Bachelor’s degree in Computer Science or related field

Master’s degree in quantitative discipline

Tools

Terraform

CloudFormation

LangSmith

Arize Phoenix

WhyLabs

OpenSearch

Pinecone

Databricks

Snowflake

Job description

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.