Job description

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 AgentOps 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, 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. Databricks and MLflow are preferred, although comparable production MLOps tools and platforms will be considered. Experience building or maintaining CI/CD pipelines. Experience with GitHub Actions is preferred. Experience with observability and monitoring technologies. AWS CloudWatch 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/DevOps engineer who transitioned into MLOps, or an ML engineer with strong infrastructure engineering experience. Experience owning or improving an ML platform at scale. Principal Duties and Responsibilities: Design, build, maintain, and scale production ML pipelines and platforms supporting 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 supporting real-time and batch model inference. Implement monitoring and observability for ML systems, including model performance, system health, reliability, and operational issues. Help expand MLOps capabilities into GenAI and AgentOps, including areas such as 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 around reproducibility, security, governance, and operational excellence. Mentor other engineers, contribute to technical design decisions, and help establish MLOps engineering standards across teams. #LI-TS1 ~IN1 Our company is built on helping individuals and families, and this starts with our employees. We want employees to maintain a positive balance, which is why we provide access to the benefits and resources they need to invest in themselves. From our onsite fitness facilities and generous paid time off to employee professional development programs, we are committed to helping employees live and work their best – both inside and outside the office. Unum is an equal opportunity employer, considering all qualified applicants and employees for hiring, placement, and advancement, without regard to a person s race, color, religion, national origin, age, genetic information, military status, gender, sexual orientation, gender identity or expression, disability, or protected veteran status. The base salary range for applicants for this position is listed below. Unless actual salary is indicated above in the job description, actual pay will be based on skill, geographical location and experience. $89,400.00-$183,500.00 Additionally, Unum offers a portfolio of benefits and rewards that are competitive and comprehensive including healthcare benefits (health, vision, dental), insurance benefits (short & long-term disability), performance-based incentive plans, paid time off, and a 401(k) retirement plan with an employer match up to 5% and an additional 4.5% contribution whether you contribute to the plan or not. All benefits are subject to the terms and conditions of individual Plans. Company: Unum

Requirements

Must-haves

Job Posting End Date: November 01
Unum is changing, and we’re excited about what’s next. Join us.
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, 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.
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.
Design, build, maintain, and scale production ML pipelines and platforms supporting 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 supporting real-time and batch model inference.
Implement monitoring and observability for ML systems, including model performance, system health, reliability, and operational issues.

Nice-to-haves

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 GitHub Actions is preferred.
Experience with observability and monitoring technologies. AWS CloudWatch and Databricks Lakehouse Monitoring experience is preferred.
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/DevOps engineer who transitioned into MLOps, or an ML engineer with strong infrastructure engineering experience.
Experience owning or improving an ML platform at scale.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

agilemachine learningaimlpythonci/cdawsdockerkubernetesllm

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Professional Development