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Strong brand, mid-level ML role in Bangalore with broad skillset demands increases candidate competition.
Core ML, Databricks, Spark, and Kubernetes skills transfer well across industries, though media experience is advantageous.
Mandatory 5+ years and specific Databricks, Spark, Kubernetes, cloud, and ML pipeline requirements raise strictness.
Design, build, and operate ML-ready data pipelines and platform services supporting AI researchers and product teams across the full ML lifecycle.
Deploy, monitor, and troubleshoot production AI/ML workflows on Kubernetes and Databricks at scale, ensuring reliability, latency, throughput, and cost targets.
Develop reusable data platform APIs, SDKs, and frameworks; own data quality, governance, observability, and metadata management for enterprise-wide ML data assets.
5+ years experience in data engineering, AI engineering, or related field with production ML pipeline ownership.
Expert proficiency in Python, Scala, or Java programming languages.
Hands-on experience with Databricks Lakehouse, Delta Lake, Apache Spark or equivalent distributed data processing, and production Kubernetes workloads.
Strong experience with cloud platforms (AWS, GCP, or Azure), advanced SQL, relational and NoSQL databases, data modeling, and event-driven systems.
Experienced in designing and operating scalable ML data pipelines enabling research and product ML workflows in production cloud-native environments.
Comfortable with end-to-end ML lifecycle operations including training, testing, validation, deployment, and inference of AI models at scale.
Solid understanding and hands-on with Databricks, distributed processing frameworks, Kubernetes operations, and cloud data architectures to optimize cost, performance, and reliability.