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

Permanent employee, Full-time · Karlsruhe, Germany
Position Description
As a (Senior) ML-Ops 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 curating datasets, evaluating machine learning models using key performance indicators (KPIs), validating, deploying and monitoring 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 infrastructure and 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 ML-Ops practices, cloud platforms, and automation tools.
In this role, you will:
  • 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
What you will need to succeed:
  • 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
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