- Own the ML lifecycle: Design implement and maintain robust containerized and reproducible pipelines for model training evaluation and deploymentacross both batch and real-time settings.
- Operationalize models at scale: Build and manage ML services APIs and model serving infrastructure using tools like MLflow Amazon SageMaker and Feature Store.
- Automate and monitor: Set up and maintain monitoring observability and alerting systems to ensure high availability and performance (including model/data drift feature logging and inference latency).
- Accelerate experimentation: Develop and maintain internal libraries templates and platform tooling to improve reproducibility and simplify deployment workflows for all model teams.
- Ensure reliability and quality: Implement CI/CD pipelines for model and data workflows using Docker Terraform and Jenkins and share best practices mentor less experienced engineers and foster strong collaboration across teams.
- Stay current: Continuously evaluate emerging MLOps technologies to improve efficiency scalability and reliability.
Qualifications :
- Hands-on MLOps experience: 2 years production experience operationalizing deploying monitoring and maintaining ML models at scale.
- Tooling: Proficient with infrastructure-as-code CI/CD systems (Docker Terraform Jenkins or equivalent) and at least one major cloud provider (AWS GCP or Azure).
- Programming: Strong Python skills (including ML libraries such as scikit-learn LightGBM PyTorch TensorFlow; plus experience with SQL).
- Monitoring: Familiar with monitoring and logging for ML pipelines (model drift detection data validation performance/feature logging).
- Collaboration: Effective communicator with experience partnering across engineering and data science.
- Bonus: Experience with feature stores model version management or building internal ML platforms.
Additional Information :
- Working from home: you can work from home up to 5 days a week
- International teams
- Educational budget to support your continuous learning and development
- Personal growth with the contribution of own ideas
- Exciting team events like Hackathons to foster collaboration and innovation
- Discounts at Autohero our platform for high-quality used cars
Contact
Maryam Anwar
At AUTO1 Group we live an open culture believe in direct communication and value diversity. We welcome every applicant; regardless of gender ethnic origin religion age sexual identity disability or any other non-merit factor.
#LI-A1
Remote Work :
Yes
Employment Type :
Full-time
Own the ML lifecycle: Design implement and maintain robust containerized and reproducible pipelines for model training evaluation and deploymentacross both batch and real-time settings.Operationalize models at scale: Build and manage ML services APIs and model serving infrastructure using tools like...
- Own the ML lifecycle: Design implement and maintain robust containerized and reproducible pipelines for model training evaluation and deploymentacross both batch and real-time settings.
- Operationalize models at scale: Build and manage ML services APIs and model serving infrastructure using tools like MLflow Amazon SageMaker and Feature Store.
- Automate and monitor: Set up and maintain monitoring observability and alerting systems to ensure high availability and performance (including model/data drift feature logging and inference latency).
- Accelerate experimentation: Develop and maintain internal libraries templates and platform tooling to improve reproducibility and simplify deployment workflows for all model teams.
- Ensure reliability and quality: Implement CI/CD pipelines for model and data workflows using Docker Terraform and Jenkins and share best practices mentor less experienced engineers and foster strong collaboration across teams.
- Stay current: Continuously evaluate emerging MLOps technologies to improve efficiency scalability and reliability.
Qualifications :
- Hands-on MLOps experience: 2 years production experience operationalizing deploying monitoring and maintaining ML models at scale.
- Tooling: Proficient with infrastructure-as-code CI/CD systems (Docker Terraform Jenkins or equivalent) and at least one major cloud provider (AWS GCP or Azure).
- Programming: Strong Python skills (including ML libraries such as scikit-learn LightGBM PyTorch TensorFlow; plus experience with SQL).
- Monitoring: Familiar with monitoring and logging for ML pipelines (model drift detection data validation performance/feature logging).
- Collaboration: Effective communicator with experience partnering across engineering and data science.
- Bonus: Experience with feature stores model version management or building internal ML platforms.
Additional Information :
- Working from home: you can work from home up to 5 days a week
- International teams
- Educational budget to support your continuous learning and development
- Personal growth with the contribution of own ideas
- Exciting team events like Hackathons to foster collaboration and innovation
- Discounts at Autohero our platform for high-quality used cars
Contact
Maryam Anwar
At AUTO1 Group we live an open culture believe in direct communication and value diversity. We welcome every applicant; regardless of gender ethnic origin religion age sexual identity disability or any other non-merit factor.
#LI-A1
Remote Work :
Yes
Employment Type :
Full-time
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