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.

Roles & 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.

Educational 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.