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Strong employer brand and Bangalore location increase candidate density, but role is specialized.
Platform and ML production skills are transferable broadly, though specific data-lakehouse and ML tooling experience matter.
Explicit 10+ years requirement plus mandatory ML production and platform tooling experience makes screening strict.
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Design, build, and facilitate adoption of a scalable Data and ML platform supporting thousands of users and self-service ML experimentation pipelines.
Develop and maintain ML pipelines covering data preparation, cataloging, feature engineering, model training, and serving that directly influence business decisions.
Collaborate with software engineers, data scientists, and threat analysts; review code; and implement workflow orchestration and cloud-native tools in a production environment.
Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics, or related field plus 10+ years experience; or Master's with 8+ years; or PhD with 6+ years.
3+ years experience developing and deploying machine learning solutions to production.
Experience with ML platform tools (e.g., Jupyter, MLFlow, Ray, Vertex AI) and big data technologies like Apache Spark or Flink in GCP.
Proficiency in distributed computing and orchestration tools (Kubernetes, Airflow), infrastructure-as-code (Terraform, FluxCD), Python expertise, CI/CD (GitHub Actions), and containerization frameworks.
Experienced in building and scaling data and ML platforms in a cloud-first, production-focused environment.
Strong coding skills in Python with ability to create simplified interfaces for data scientists and champion software engineering best practices.
Comfortable working in a cross-functional setting with diverse stakeholders to synthesize requirements and deliver reliable ML infrastructure.