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Metro Bengaluru, broad data/ML requirements, and common Data Engineer title increase applicant competition.
Core data engineering and ML skills are transferable, though automotive domain knowledge slightly increases sensitivity.
Explicit 6-9 years requirement plus mandatory data/ML/cloud skills and tooling raises screening rigidity.
Design, build, and maintain large-scale data pipelines and infrastructure focused on AI/ML applications.
Collaborate with data scientists to deploy and monitor machine learning models and optimize data storage for performance and cost.
Ensure data quality, security, and governance across data platforms used by data science and engineering teams.
6-9 years of data engineering experience with a focus on AI/ML.
Proficient in Python, SQL and data technologies such as Hadoop and Spark.
Bachelor's degree in Computer Science or related field.
Experience with ML frameworks (e.g., TensorFlow, PyTorch) and cloud platforms (AWS, GCP, Azure).
Experienced in building and scaling large data systems supporting AI/ML workflows for real-time or batch processing.
Comfortable working in a collaborative, cross-functional environment with data scientists and engineers to deliver optimized ML solutions.
Capable of managing data governance and security in enterprise-grade data platforms, likely with prior exposure to automotive or embedded system data contexts.