Senior MLOps Engineer

Job Posting End Date:
November 01

Our Fortune 500 company is driving a digital transformation and looking for forward-thinking innovators to disrupt how our industry thinks about and uses technology. As one of the world's leading employee benefits providers, we help millions of people gain affordable access to benefits that help them protect their families, their finances and their futures.

Are you an asker of questions, a solver of problems, and a challenger of the status quo? Our mission is to provide a differentiated customer experience and exceed the expectations people have of technology at any company — not just insurers.

We are seeking individuals to join our team of talented IT professionals who share never-ending passion and an unwavering focus on our customer experience. Team members comfortable working in an agile, fast-paced, and delivery-focused environment thrive in our environment where we value an entrepreneurial spirit and those who challenge the status-quo.

Unum is changing, and we’re excited about what’s next. Join us.

General

Summary:

We’re looking for a Senior MLOps Engineer to help build, scale, and evolve the platforms that take machine learning models from experimentation to reliable production systems. This is primarily an MLOps role with exposure to GenAI and agentic AI.You’ll work closely with data science and engineering teams to maintain and improve existing ML platforms, automate the model lifecycle, strengthen deployment and monitoring capabilities, and build new MLOps and Agent Ops capabilities.

You’ll also help establish technical standards and mentor other engineers as the organization continues to mature its ML and AI engineering practices.

Job Specifications:

Bachelor’s degree in Computer Science, Information Technology, Information Systems, a related field, or equivalent relevant work experience.
5+ years of experience in software engineering, Dev Ops, ML engineering, MLOps, or a related discipline, with demonstrated experience supporting machine learning systems in production.

Strong Python programming skills and software engineering fundamentals.

Hands-on production MLOps experience across CI/CD, model deployment, model serving, and monitoring.

Hands-on experience with AWS and cloud-based infrastructure.

Strong experience with Docker and Kubernetes.

Experience with Infrastructure as Code, such as Terraform.

Hands-on experience contributing to at least one LLM or agentic AI project, such as a proof of concept, pilot, or production implementation. Relevant experience may include RAG, evaluation, versioning, monitoring, tracing, cost management, or guardrails.

Experience with ML lifecycle and platform technologies. Databricks and MLflow are preferred, although comparable production MLOps tools and platforms will be considered.

Experience building or maintaining CI/CD pipelines.

Experience with Git Hub Actions is preferred.

Experience with observability and monitoring technologies. AWS Cloud Watch and Databricks Lakehouse Monitoring experience is preferred.

Strong communication and collaboration skills with the ability to work effectively across data science and engineering teams.

Ability to mentor other engineers, influence technical direction, and help establish engineering standards without formal people-management responsibility.

Preferred Qualifications:

Hands-on experience with Databricks, including capabilities such as Unity Catalog, Jobs/Workflows, Model Serving, and Lakehouse Monitoring.

Experience with MLflow for ML lifecycle management.

Experience supporting GenAI or agentic AI solutions beyond initial experimentation.

Experience with AI/agent observability, evaluation, tracing, versioning, cost management, or guardrails.

Background as a software/Dev Ops…