Senior MLOps Engineer

Unum • Portland, ME

5d • $89,400 - $183,500

About The Position

Unum is seeking a Senior MLOps Engineer to build, scale, and evolve platforms that move machine learning models from experimentation into reliable production systems. The role is primarily focused on MLOps, with exposure to generative AI and agentic AI. The engineer will work closely with data science and engineering teams to maintain and improve ML platforms, automate the model lifecycle, strengthen deployment and monitoring capabilities, and develop MLOps and AgentOps capabilities. The role also contributes to technical standards and mentoring as the organization advances its ML and AI engineering practices.

The position involves designing and operating production ML pipelines and infrastructure for real-time and batch inference; improving CI/CD, model lifecycle management, observability, and platform reliability; and helping teams apply operational practices to GenAI and agentic AI solutions. The engineer partners across teams, influences technical direction, and promotes reproducibility, security, governance, and operational excellence without formal people-management responsibility.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Information Systems, a related field, or equivalent relevant work experience.
• At least 5 years of experience in software engineering, DevOps, 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. Comparable production MLOps tools and platforms are considered alongside Databricks and MLflow.
• Experience building or maintaining CI/CD pipelines.
• Experience with observability and monitoring technologies.
• 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.

Nice To Haves

• Databricks experience, including Unity Catalog, Jobs/Workflows, Model Serving, and Lakehouse Monitoring.
• MLflow experience for ML lifecycle management.
• Experience with GitHub Actions.
• Experience with AWS CloudWatch and Databricks Lakehouse Monitoring.
• 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 or DevOps engineer who transitioned into MLOps, or as an ML engineer with strong infrastructure engineering experience.
• Experience owning or improving an ML platform at scale.

Responsibilities

• Design, build, maintain, and scale production ML pipelines and platforms for model deployment, serving, monitoring, and lifecycle management.
• Build and improve CI/CD workflows for ML models, including automated testing, versioning, deployment, and release processes.
• Develop and operate scalable infrastructure for real-time and batch model inference.
• Implement monitoring and observability for ML systems, including model performance, system health, reliability, and operational issues.
• Expand MLOps capabilities into GenAI and AgentOps, including evaluation, versioning, monitoring, tracing, cost management, and guardrails.
• Maintain and improve infrastructure using Infrastructure as Code, including Terraform or comparable technologies.
• Support ML lifecycle capabilities such as experiment tracking, model registries, model serving, and ML workflow orchestration.
• Partner with data scientists and engineers to improve the reliability, scalability, and maintainability of production ML solutions.
• Drive continuous improvements to deployment processes, monitoring, platform reliability, and developer experience.
• Establish and promote engineering standards for reproducibility, security, governance, and operational excellence.
• Mentor other engineers, contribute to technical design decisions, and help establish MLOps engineering standards across teams.

Benefits

• Onsite fitness facilities.
• Paid time off.
• Employee professional development programs.
• Healthcare benefits, including health, vision, and dental.
• Short- and long-term disability insurance benefits.
• Performance-based incentive plans.
• 401(k) retirement plan with an employer match up to 5% and an additional 4.5% contribution whether or not the employee contributes.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior