(Senior) ML-Ops Engineer (f/m/d)

Salary: 65.000 - 100.000 per year

Requirements:

• Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management
• Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions.
• Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines)
• Proficiency in building ML-Pipelines for productive use
• IaC (Infrastructure as Code) coding experience for provisioning relevant resources locally and in the cloud.
• Basic ML-knowledge is a plus
• Strong programming skills in Python
• Strong verbal and written communication skills in English

Responsibilities:

• As a (Senior) MLOps Engineer, you will play a crucial role in building and maintaining the infrastructure and processes required to support machine learning operations. You will be responsible for preparing datasets, evaluating machine learning models using key performance indicators (KPIs), validating and deploying models, and ensuring their seamless integration into production systems (embedded and Cloud). Additionally, you will design and implement CI/CD pipelines for machine learning, automate ML operations, and utilize cloud-based solutions, such as AWS with Terraform, to enhance scalability and efficiency. This position requires a proactive individual with a strong foundation in MLOps practices, cloud platforms, and automation tools.
• Provide a dataset infrastructure and implement interfaces to support machine learning model development and training
• Deploy machine learning models into production environments and develop versioned rollout strategy on-device and in the cloud
• Ensure maximum availability of ML-Models in the cloud at an appropriate scale
• Gather and provide KPIs for a productive running model for continuous quality checks by the ML-Engineers
• Design, develop, and maintain CI/CD pipelines to streamline ML model development and deployment workflows
• Automate repetitive and manual processes involved in machine learning operations to improve efficiency
• Implement and manage in-cloud ML-Ops solutions, leveraging Terraform for infrastructure as code

Technologies:

• Airflow
• AWS
• BitBucket
• CI/CD
• Cloud
• Embedded
• GitHub
• Support
• Kubeflow
• Machine Learning
• Mobile
• Python
• Terraform
• C++
• DevOps

More:

Cinemo is a global provider of highly innovative infotainment products that make every screen an opportunity. Its range of award-winning, fully integrated, low-footprint digital media offerings combine high performance with high quality and are truly system agnostic. Whether embedded, as mobile apps or through the cloud, Cinemo supports all digital media scenarios for any industry and any device. Its product portfolio is designed and built to deliver excellence, accelerate time to market, and lower TCO for its clients while creating digital media experiences that matter.

Founded in 2008, and with a strong history of industry firsts, Cinemo is the partner of choice for more than 40 market-leading OEMs and over 20 tier-1s. The company works with the top high-tech and consumer electronic companies as well as global music and video content providers. Cinemos global team of 300+ innovative thinkers from 40 nationalities continuously delivers groundbreaking innovation.

last updated 1 week of 2026