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Strong employer brand and Bangalore metro location increase applicant density despite senior, niche role.
Requires specialized data lakehouse/ML platform skills (Iceberg, Spark, ML pipelines, Kubernetes), so cross-industry transferability is low.
Explicit 10+ years requirement plus mandatory ML production experience and specific tooling makes shortlisting highly strict.
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Design and develop scalable ML experimentation platforms and pipelines supporting data preparation, feature engineering, model training, and model serving.
Collaborate across teams to implement and maintain production ML pipelines that influence critical business decisions.
Build platform capabilities for thousands of users, enabling self-service and operationalizing ML models with best practices in CI/CD and distributed systems.
Bachelor's in Computer Science, Data Science, Statistics, Applied Mathematics, or related field with 10+ years experience; OR Master's with 8+ years; OR PhD with 6+ years.
Minimum 3+ years experience deploying machine learning solutions to production.
Proficiency in Python (expert), CI/CD frameworks (GitHub Actions), containerization, and distributed computing technologies (Kubernetes, Airflow).
Experience with ML platform tools (e.g., Jupyter, MLflow), data platform tools (Apache Spark, Flink or comparable), and infrastructure-as-code tools (Terraform, FluxCD).
Experienced in building and scaling ML pipeline platforms integrating data engineering and ML ops for large-scale, multi-team environments.
Strong cross-functional collaborator able to translate stakeholder needs into software solutions within a cloud-first environment.
Comfortable with distributed data processing, modularization of complex ML systems, and production-grade software engineering best practices.