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Tier-1 brand, Bangalore location, and broad ML/platform skill requirements elevate competition.
Skills like Spark, Kubernetes, and ML pipelines transfer across industries, though cybersecurity context adds moderate specificity.
High due to explicit seniority requirement, mandatory ML production experience, and specific platform tooling.
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Design, build, and facilitate adoption of a scalable Data and ML platform supporting ML experimentation pipelines for thousands of users.
Collaborate with Data Platform Engineers, Data Scientists, and Threat Analysts to implement ML pipelines for data prep, feature engineering, training, and serving that impact business decisions.
Own development and operational success of ML Experimentation Platform including workflow orchestration, cloud services (Kubernetes, blob storage), and adoption of software development best practices.
B.S. in Computer Science or related field with 10+ years' experience OR M.S. with 8+ years OR Ph.D. with 6+ years experience.
Minimum 3 years developing and deploying machine learning solutions to production with working familiarity of ML algorithms and supervised/unsupervised approaches.
3+ years experience with ML platform tools such as Jupyter Notebooks, MLFlow, Ray, or Vertex AI.
Experience building data platform features using Apache Spark, Flink, or similar tools on GCP; proficiency in distributed computing and orchestration (Kubernetes, Airflow); strong Python skills; expertise with CI/CD and containers. Notice period: Not explicitly mentioned in the JD.
Experience working in production ML platforms or data engineering teams using large scale distributed data systems and cloud-native technologies.
Ability to modularize complex ML code into reusable components and champion engineering best practices in collaboration with data scientists and engineers.
Experienced in building platforms focused on self-service, scalability, and workflow orchestration with strong analytical and problem solving skills in a dynamic environment.