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

<h3>Position Description</h3> <p>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.</p> <h3>In this role, you will:</h3> <ul><li><span><span>P</span><span>rovide a</span><span> dataset</span><span> infrastructure and implement interfaces</span><span> </span><span>to support machine learning model development and training</span></span></li><li><span><span>Deploy machine learning models into production environments </span><span>and </span><span>develop</span><span> versioned ro</span></span><span><span>llou</span><span>t</span><span> strategy </span></span><span><span>on</span><span>-device and in the cloud</span></span></li><li><span><span>E</span><span>nsure </span><span>maximum availability</span><span> of ML-Models in the cloud</span><span> at </span><span>an appropriate scale</span></span></li><li><span><span>G</span></span><span><span>ather and p</span></span><span><span>rovide KPIs for a productive running model for continuous quality checks by the ML-Engineers</span></span></li><li><span><span>Design, develop, and </span><span>maintain</span><span> CI/CD pipelines to streamline ML model development and deployment workflows</span></span></li><li><span><span>Automate repetitive and manual processes involved in machine learning operations to improve efficiency</span></span></li><li><span><span>Implement and man</span><span>age </span><span>in-c</span><span>loud </span><span>ML</span><span>-</span><span>Ops</span><span> solut</span><span>ions</span><span>,</span><span> </span><span>leveraging</span><span> Terraform for infrastructure as code</span></span></li></ul> <h3>What you will need to succeed:</h3> <ul><li><span><span>Minimum</span><span> </span><span>1 to 2 </span><span>years of proven experience</span><span> in </span><span>ML</span><span>-</span><span>Ops</span><span>, including end-to-end machine learning lifecycle management</span></span></li><li><span><span>Familiarity with </span><span>MLOps</span><span> tools like </span><span>MLFlow</span><span>, Airflow, Kubeflow</span><span> or custom implemented </span><span>solutions</span><span>.</span></span></li><li><span><span>Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., </span><span>Github</span><span> actions, Bitbucket Pipelines</span><span>)</span></span></li><li><span><span>Proficiency</span><span> in building ML-Pipelines for productive use </span></span></li><li><span><span>IaC</span><span> (</span><span>Infrastructure as</span><span> Code) coding experience for </span><span>provisioning</span><span> relevant resources local</span><span>ly</span><span> and </span><span>in the </span><span>cloud</span><span>.</span><span>  </span></span></li><li><span><span>Basic ML-</span><span>k</span><span>nowle</span><span>d</span><span>ge</span><span> </span><span>is a plus</span></span></li><li><span><span>Strong programming skills in Python</span></span></li><li><span><span>Strong verbal and written communication skills in English</span></span></li></ul>