(Senior) ML-Ops Engineer (f/m/d) at Cinemo GmbH

Karlsruhe, Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

14 Feb, 26

Salary

0.0

Posted On

16 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

ML-Ops, Machine Learning, CI/CD Pipelines, Cloud Platforms, Automation Tools, Python, MLFlow, Airflow, Kubeflow, Infrastructure as Code, KPI Evaluation, Model Deployment, Model Monitoring, Dataset Curation, Terraform, Version Control

Industry

Embedded Software Products

Description

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

Responsibilities

As a (Senior) ML-Ops Engineer, you will build and maintain the infrastructure and processes for machine learning operations. You will deploy and monitor models, ensuring their integration into production systems and designing CI/CD pipelines for machine learning.