This free MLOps Engineer job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring someone to own the infrastructure and tooling that lets your team train, deploy, and monitor models reliably — the operational backbone of ML. It blends DevOps and ML, and matters most once you have models in production at scale.
MLOps candidates want to know your ML maturity, the scale, and your stack. Be specific about what exists versus what they'll build, and how the role relates to ML engineering and platform teams.
If the role is mainly building models, use the Machine Learning Engineer template; if it's general infrastructure, use the DevOps or Site Reliability Engineer template.
Writing tips
- Describe your ML maturity, scale, and current stack.
- Clarify what exists vs. what they'll build, and the relationship to ML engineering.
- Emphasize reliability, reproducibility, and automation of the ML lifecycle.
- Name your tooling (orchestration, serving, monitoring, feature stores).
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. As our use of ML grows, we're hiring an MLOps Engineer to build the infrastructure that lets us ship and operate models reliably.
The role
As an MLOps Engineer, you'll build and own the platform behind our ML — training and deployment pipelines, serving infrastructure, and monitoring. You'll make models reproducible, reliable, and easy to ship. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Build and maintain training, deployment, and inference pipelines.
- Own model serving infrastructure and its reliability and cost.
- Set up monitoring for performance, drift, and data quality.
- Make ML workflows reproducible and easy for scientists to use.
- Partner with ML engineers and scientists to operationalize models.
What we're looking for
- 3+ years in MLOps, ML platform, or DevOps with ML exposure.
- Strong software and infrastructure skills (cloud, containers, IaC).
- Experience with ML pipelines, serving, and monitoring.
- A focus on reliability, reproducibility, and automation.
- The ability to partner with scientists and ML engineers.
Nice to have
- Experience with [your ML tooling, e.g. MLflow, Kubeflow, SageMaker].
- Familiarity with feature stores and experiment tracking.
- Background scaling ML infrastructure.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- Ownership of the platform that makes our ML possible.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does an MLOps Engineer do?
- An MLOps Engineer builds and owns the infrastructure that runs machine learning in production — training and deployment pipelines, model serving, and monitoring for performance and drift. They make ML workflows reproducible, reliable, and easy for scientists and engineers to use.
- What's the difference between an MLOps Engineer and a Machine Learning Engineer?
- A Machine Learning Engineer focuses on building and deploying models. An MLOps Engineer focuses on the platform and tooling that make training, deploying, and monitoring models reliable and repeatable across the team. MLOps is to ML what DevOps is to software.
- What skills should an MLOps Engineer have?
- Strong software and infrastructure skills (cloud, containers, infrastructure as code), experience with ML pipelines, serving, and monitoring, and a focus on reliability, reproducibility, and automation. Familiarity with MLOps tooling like MLflow or Kubeflow is a common plus.