Work at the forefront of vehicle development with state-of-the-art data/ML solutions.
Make a tangible impact on vehicle performance safety and driver experience through robust production systems.
Collaborate in an international cooperative environment with clear growth and development paths.
We value ownership transparency and continuous improvement enabling you to showcase your expertise.
Join BMWTechWorks India within the Digital Product Engineering cluster in the Unit Methods/Tools to design deploy and evolve production-grade ML models and data pipelines for the Digital Tire Diagnosis product.
Own end-to-end ML solutions: data pipelines model training deployment monitoring and continuous improvement.
Compare model families optimize hyperparameters validate performance and maintain versioned releases in production.
Build robust ETL/feature pipelines (batch and streaming) curate data assets and implement automated tests observability and CI/CD practices.
Develop dashboards to monitor model and pipeline performance conduct robustness and security checks and ensure cloud governance and data asset hygiene.
What should you bring along
A hands-on end-to-end mindset with ownership for production ML systems from data ingestion to monitoring and governance.
Strong collaboration skills to work in cross-functional data-driven product teams.
Ability to document complex architectures clearly and concisely for stakeholders and governance bodies.
Proactive detail-oriented approach with a focus on stability security and regulatory/compliance requirements.
Must have technical skill
Proficiency in Python PySpark SQL; solid experience with modern ML frameworks (TensorFlow PyTorch scikit-learn).
Hands-on with AWS services (S3 Glue IAM Lambda Athena) and cloud data governance practices; experience with CDH/Hive environments.
Experience with Spark-based pipelines; familiarity with Palantir Foundry is a plus.
Knowledge of model monitoring explainable AI drift/bias analysis and ML security practices.
Experience implementing CI/CD for ML/data projects Git workflows infrastructure as code and QA/test frameworks.
Ability to design implement and document scalable data assets (schemas partitioning data quality rules catalog entries).
Strong communication skills to articulate architecture and governance requirements to technical and non-technical stakeholders.
Good to have Technical skills
Experience with adversarial testing robustness analyses and threat modelling.
Familiarity with dashboards and observability tools for ML models and data pipelines (e.g. drift metrics SLAs alerts).
Prior exposure to automated testing strategies for data and ML components.
Knowledge of data privacy security compliance standards and industry best practices in automotive/industrial contexts.
Experience with scheduling and orchestration tools (Foundry Schedules Airflow-style tooling) for end-to-end data workflows.
Required Experience:
Manager
RoleSenior ML Engineer Production ML & Data EngineeringVisit our websiteto know more.Follow us onLinkedInIInstagramIFacebookIXfor the exciting updates.About the UNIT/ Unit OverviewDigital Product EngineeringLocationPuneExperience: 5Number of openings1What awaits you/ Job ProfileWork at the forefront...
Role
Senior ML Engineer Production ML & Data Engineering
Work at the forefront of vehicle development with state-of-the-art data/ML solutions.
Make a tangible impact on vehicle performance safety and driver experience through robust production systems.
Collaborate in an international cooperative environment with clear growth and development paths.
We value ownership transparency and continuous improvement enabling you to showcase your expertise.
Join BMWTechWorks India within the Digital Product Engineering cluster in the Unit Methods/Tools to design deploy and evolve production-grade ML models and data pipelines for the Digital Tire Diagnosis product.
Own end-to-end ML solutions: data pipelines model training deployment monitoring and continuous improvement.
Compare model families optimize hyperparameters validate performance and maintain versioned releases in production.
Build robust ETL/feature pipelines (batch and streaming) curate data assets and implement automated tests observability and CI/CD practices.
Develop dashboards to monitor model and pipeline performance conduct robustness and security checks and ensure cloud governance and data asset hygiene.
What should you bring along
A hands-on end-to-end mindset with ownership for production ML systems from data ingestion to monitoring and governance.
Strong collaboration skills to work in cross-functional data-driven product teams.
Ability to document complex architectures clearly and concisely for stakeholders and governance bodies.
Proactive detail-oriented approach with a focus on stability security and regulatory/compliance requirements.
Must have technical skill
Proficiency in Python PySpark SQL; solid experience with modern ML frameworks (TensorFlow PyTorch scikit-learn).
Hands-on with AWS services (S3 Glue IAM Lambda Athena) and cloud data governance practices; experience with CDH/Hive environments.
Experience with Spark-based pipelines; familiarity with Palantir Foundry is a plus.
Knowledge of model monitoring explainable AI drift/bias analysis and ML security practices.
Experience implementing CI/CD for ML/data projects Git workflows infrastructure as code and QA/test frameworks.
Ability to design implement and document scalable data assets (schemas partitioning data quality rules catalog entries).
Strong communication skills to articulate architecture and governance requirements to technical and non-technical stakeholders.
Good to have Technical skills
Experience with adversarial testing robustness analyses and threat modelling.
Familiarity with dashboards and observability tools for ML models and data pipelines (e.g. drift metrics SLAs alerts).
Prior exposure to automated testing strategies for data and ML components.
Knowledge of data privacy security compliance standards and industry best practices in automotive/industrial contexts.
Experience with scheduling and orchestration tools (Foundry Schedules Airflow-style tooling) for end-to-end data workflows.
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