Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.
Job DescriptionRoles & Responsibilities :
About the Role
We are looking for a highly skilled Data/ML Engineer to design and maintain secure, scalable, and high-performance data pipelines and ML infrastructure. The ideal candidate will have strong expertise in Python, SQL, data processing frameworks, ETL pipelines, and MLFlow integration, with hands-on experience in real-time data streaming, telemetry storage, and secure data handling.
Key Responsibilities
Design, implement, and maintain secure ETL pipelines for large-scale data processing.
Develop and optimize data pipelines using Apache Airflow for scheduling and orchestration.
Work with Kafka/MQTT for real-time data ingestion and processing.
Manage time-series and telemetry data storage with InfluxDB/TimescaleDB.
Implement data encryption, signing, and security best practices for sensitive data pipelines.
Integrate and manage MLFlow for model tracking, experiment management, and deployment.
Use Python, SQL, and Pandas for data transformation, analysis, and integration with ML workflows.
Collaborate with data scientists and ML engineers to ensure efficient and secure model deployment.
Monitor and optimize performance of data pipelines and ML infrastructure.
QualificationsEducational qualification: B.E/B.Tech
Experience : 5-7 Years
Mandatory/requires Skills :
5–7 years of experience as a Data Engineer, ML Engineer, or in related roles.
Proficiency in Python, SQL, and Pandas for data analysis and processing.
Strong hands-on experience with Kafka/MQTT for real-time data streaming.
Expertise in InfluxDB/TimescaleDB for time-series data handling.
Experience with Apache Airflow for workflow orchestration.
Knowledge of secure ETL pipelines, encryption, and data signing practices.
Familiarity with MLFlow integration for ML lifecycle management.
Strong understanding of telemetry storage and processing.
Good to Have
Experience with cloud-based data platforms (AWS, GCP, Azure).
Knowledge of containerization and orchestration tools (Docker, Kubernetes).
Experience in monitoring tools and performance optimization for large-scale data systems.