Job Description Design, integrate, and maintain efficient and scalable data pipeline architecture to support business needs. Develop and manage large, complex data sets that align with functional and non-functional business and management requirements. Develop AI solutions and provide support to AI strategy. Identify opportunities for internal process improvements, including automating manual tasks, optimizing data delivery, and redesigning data infrastructure for enhanced scalability and performance. Architect and review data structures to ensure optimal extraction, transformation, and loading (ETL) of data from diverse sources, utilizing SQL and advanced big‑data technologies. Build and implement analytics tools leveraging the data pipeline to provide actionable insights on operational efficiency and key business performance metrics. Collaborate with cross‑functional teams, including Management, Product, Data, and Design, to address data‑related technical challenges and support their data needs. Ensure data security and privacy by maintaining access controls, safeguarding data integrity, and ensuring data is accessible only to authorized personnel. Develop and maintain digital solutions with DS and/or AI to support analytics and data science teams in optimizing and innovating product offerings. Design, maintain, and ensure the effective operation of Data Warehouse and Data Lake structures (including JuP1 Data Lake). Collaborate with the Data Management team to uphold data governance standards. Ensure compliance with data quality principles and best practices. Actively promote and support the Bosch Production System, contribute to Continuous Improvement Projects (CIP), policy deployment, and foster teamwork. Oversee labor discipline within the team, if applicable. Adhere to and enforce Environmental Health and Safety (EHS) policies, ensuring compliance with legal and corporate safety regulations. Qualifications Computer Science engineer or AI engineering Programming: R, Java, C++ (depending on the environment) Machine Learning and Deep Learning: Frameworks: TensorFlow, PyTorch, Scikit-learn Cloud and DevOps: MLOps: Complementary Skills NLP, Computer Vision (depending on the role) Optimization and algorithms Model quality control and testing Knowledge of AI ethics and governance Recommended Certifications Google Professional Machine Learning Engineer TensorFlow Developer Certificate #J-18808-Ljbffr