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Medium — strong employer brand and metro location increase applicant density despite senior specialization.
Medium — ML platform skills transfer across industries, though hyperscale cybersecurity data experience is preferred.
High — explicit 10+ years requirement plus senior leadership and specific ML platform/MLOps experience.
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Lead and define vision and roadmap for the Data and ML Platform to support scalable ML pipelines for data preparation, feature engineering, model training, serving, and monitoring.
Build and manage a cross-functional team of Data and ML Platform engineers focused on AI/ML infrastructure and Generative AI investments.
Drive adoption of ML Ops best practices, data governance, and operational excellence with focus on high reliability, performance, and stakeholder engagement.
Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics, or related field with 10+ years experience; MS with 8+ years or PhD with 6+ years acceptable.
10+ years experience in data engineering, ML platform development or related fields, including at least 5 years in leadership roles.
Hands-on familiarity with ML algorithms, ML platform tools (e.g., Jupyter Notebooks, MLFlow, Ray, Vertex AI), and modern ML Ops platforms (e.g., Kubeflow, SageMaker).
Experience with data platform technologies such as Apache Spark, Flink, Kubernetes, Airflow; knowledge of Apache Iceberg is a plus.
Senior technical leader with proven experience building and scaling ML and data infrastructure teams across global locations (including India and US).
Strategic thinker skilled at translating complex business needs into scalable data and ML platform capabilities, with strong operational focus on monitoring and reliability.
Experienced in deploying Generative AI and AI/ML technologies to accelerate development lifecycle and operationalize ML workflows effectively.