Back
Strong Tier-1 brand plus Bangalore location yields moderate competition among senior platform candidates.
Specialized ML platform and security-scale experience moderately limit cross-industry transferability.
Explicit 10+ years and 5+ years leadership plus specific MLOps and platform tool requirements increases strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead and define the vision, strategy, and roadmap for CrowdStrike's Data and ML Platform, focusing on building scalable ML pipelines for preparation, feature engineering, training, deployment, and model monitoring.
Build and manage a distributed team in Bengaluru and the US, responsible for developing a modern Data+ML/Generative AI platform supporting Data Platform Engineers, Data Scientists, and Threat Analysts.
Drive adoption of ML Ops best practices, implement data governance, foster operational excellence via SLI/SLO metrics, and partner with cross-functional teams to translate requirements into platform capabilities.
10+ years in data engineering, ML platform development or related fields with at least 5 years in a leadership role.
Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or related field with 10+ years experience; or MS with 8+ years; or PhD with 6+ years experience.
Experience with ML platform tools (e.g., Jupyter Notebooks, MLFlow, Ray, Vertex AI), ML Ops platforms (MLFlow, Kubeflow, SageMaker), and data platform tools (Apache Spark, Flink, Kubernetes, Airflow).
Work Experience Required: 10+ years overall experience as above; Notice period: Not explicitly mentioned in the JD.
Strategic leader with hands-on expertise in designing and scaling ML experimentation platforms within hyperscale data environments (500+ PB data).
Experienced in building and growing engineering teams across geographies and in driving technical innovation in AI/ML infrastructure and Generative AI.
Operationally oriented, able to set SLIs/SLOs and ensure high reliability and performance while collaborating across product, engineering, and data science teams.