MLOps Engineer | eBike

Bosch eBike Systems is expanding its digital offerings for eBike-manufacturers and end users including software cloud APIs and analytics/connected-services that complement the physical eBike hardware. The projects goal is to enable smarter data-driven eBike services involving signal processing embedded AI or data analytics for usage patterns predictive maintenance safety features or personalized rider experiences.

As an MLOps Engineer you will build and maintain the infrastructure and workflows required to take machine learning models from development to production: experiment tracking training pipelines evaluation model deployment and monitoring for services that integrate with eBike hardware cloud services and user applications. You will work closely with a multi-disciplinary team to ensure that ML-based features are scalable maintainable and compliant with Boschs quality and performance standards. Your work will help drive the next generation of intelligent eBike services and contribute to Boschs goal of sustainable connected mobility

Your contribution to something big: 

• Implement and/or integrate tools to deliver a state-of-the-art MLOps solution on Azure according to best practices;
• Help design setup and maintain an on-premises data collection environment for electric bicycles and collect data for model testing;
•  Design implement integrate and maintain ETL pipelines to bring video and IMU data from on-premises and AWS environments to Azure Blob and ML tables;
•  Develop and implement monitoring dashboards and alerts to proactively identify and resolve platform issues;
• Implement robust security measures to protect sensitive data and ML models in Azure
• Work closely with machine learning engineers data scientists software engineers and infrastructure teams to deliver a high-quality solution;
•  Communicate effectively with stakeholders at all levels including technical and nontechnical audiences;
•  Stay up to date with the latest MLOps trends and technologies and share knowledge and expertise with colleagues working on other projects and domains;
•  Participate in code reviews and contribute to the development of best practices.

Qualifications :

What distinguishes you:

• MSc or Ph.D. in Computer Science Data Science Machine Learning or a related field;
• 2 years of experience in MLOps Data Engineering Cloud Engineering or similar roles with a strong focus on AI/ML use-case;
• Knowledge of either industrialization laboratory environment or ADAS domain is a major plus;
• Good scripting and automation skills with Python and Bash. Proficiency with Pandas Pydantic Pytest and NumPy is a plus.;
• Capable of implementing integrating and maintaining Data and MLOps components. Architectural skills are a plus;
•  Good understanding of Cloud solutions and associated concepts. Experience with Azure Azure Machine Learning and Azure Databricks is a plus;
• Good understanding of DevOps principles and practices. Proficiency with GitOps containerization and orchestration tools like Docker Kubernetes and Apache Airflow is a plus;
• Good understanding of CI/CD pipelines and IaC tools. Proficiency with Jenkins GitHub Actions and Terraform is a plus;
• Basic experience with on-premises infrastructure management Linux servers networking and storage;
• Excellent problem-solving skills;
• Effective communication skills team player and capable of collaborating across functional areas;
•  Proactive customer and result-oriented personality.

Additional Information :

Work #LikeABosch means:

Flexible work conditions

Hybrid work system

Exchange with colleagues around the world

Health insurance and medical office on site

Training opportunities

Opportunities for career progression and continuous professional development

Access to great discounts in partnerships and Bosch products

Sports and health related activities

Great access to public transports

Free parking lot

Canteen

Success stories dont just happen. They are made...

Make it happen! We are looking forward to your application!

Remote Work :

No

Employment Type :

Full-time