Hiring the right DevOps or MLOps Engineer is essential for companies that need reliable delivery, scalable infrastructure and better collaboration between development, operations and machine learning teams. These roles are often close to production environments, so technical experience and practical judgement matter.
At NBS, we help companies recruit DevOps and MLOps professionals with the right mix of automation, infrastructure, deployment and operational experience. We focus on understanding your technical environment, the maturity of your platform, the tools already in place and the level of ownership expected before starting the search.
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DevOps / MLOps Recruitment is a specialised hiring service focused on finding professionals who improve software delivery, infrastructure reliability, automation, deployment processes and machine learning operations. Depending on the company and the structure of the team, this can include DevOps Engineers, MLOps Engineers, Platform Engineers, Cloud Engineers, Infrastructure Automation Engineers, Site Reliability Engineers or Senior DevOps professionals.
We review the technical requirements of the role, the level of seniority, the cloud environment, the deployment model, the automation needs and the level of responsibility expected. Some companies need experience with CI/CD pipelines, Docker, Kubernetes, Terraform, GitLab CI/CD, GitHub Actions, Jenkins, AWS, Azure, Google Cloud, monitoring, observability or infrastructure as code.
For MLOps roles, we also pay attention to experience with machine learning deployment, model lifecycle management, deployment and monitoring pipelines, scalable environments and collaboration with AI, data science or engineering teams. The goal is not only to find someone who knows cloud tools, but someone who can support reliable delivery and keep complex technical systems working in real conditions.
Looking for DevOps or MLOps talent with real production experience?
We can help you clarify the role, understand the market and identify candidates aligned with your technical environment.
Need a realistic view of the DevOps and MLOps talent market?
We can help you assess the role, the salary range and the type of candidates available before starting the process.
We can help companies recruit DevOps Engineers, MLOps Engineers, Platform Engineers, Cloud Engineers, Infrastructure Automation Engineers, Site Reliability Engineers and Senior DevOps professionals. The exact profile depends on your platform, deployment model, cloud environment and level of responsibility needed.
A DevOps Engineer usually needs experience with CI/CD pipelines, automation, cloud infrastructure, containers, monitoring, infrastructure as code and collaboration with development teams. Depending on the role, skills in Docker, Kubernetes, Terraform, Jenkins, GitLab, GitHub Actions, AWS, Azure or Google Cloud may be relevant.
An MLOps Engineer usually needs experience with machine learning deployment, model lifecycle management, monitoring pipelines, scalable environments and collaboration with data science, AI or engineering teams. Depending on the role, knowledge of Python, cloud platforms, containers, orchestration tools and ML platforms may also be important.
DevOps usually focuses on software delivery, automation, infrastructure, deployment and reliability. MLOps applies similar operational principles to machine learning systems, including model deployment, monitoring, versioning and lifecycle management. The two areas can overlap, especially in companies building AI or ML products.
We review their previous experience, technical stack, production exposure, level of autonomy, cloud knowledge, automation practices, incident experience and collaboration with developers or machine learning teams. We also look at motivation, communication, salary expectations and fit with the company.
These roles require a mix of technical depth, operational judgement and collaboration skills. Hiring becomes difficult when the role is not clearly defined, when DevOps, platform, cloud and systems responsibilities are mixed without priority, or when an MLOps role expects one person to cover both machine learning and production operations without a realistic scope.
