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Strong Tier-1 brand and metro location with senior ML leadership yields moderate competition.
Specialized ML engineering, MLOps, and platform leadership make cross-industry transfer difficult.
Explicit seniority plus mandatory ML platform, production, cloud, and leadership experience creates very strict filters.
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Lead and manage a team of Machine Learning Engineers and Data Scientists building and operating large-scale ML systems for critical consumer platform capabilities.
Own end-to-end delivery of ML/Data Science projects including platform lifecycle capabilities such as pipeline orchestration, experimentation, model deployment, and observability to ensure scalable, reliable production ML systems.
Collaborate with Product, Engineering, and Business teams to align ML solutions with business outcomes, while setting quality standards and driving operational excellence.
12-15 years total experience with at least 3 years managing ML/Data Science teams.
Proven hands-on experience designing and deploying large-scale machine learning systems and ML platforms in production environments.
Hands-on experience with cloud platforms such as AWS, GCP, or Azure and expertise in ML platform architectures including pipelines, experimentation systems, model versioning, deployment, and observability.
Work Experience Required: 12-15 years as stated in JD. Notice period: Not explicitly mentioned in the JD.
Experienced leader comfortable balancing people management and technical hands-on work in ML engineering and data science across large-scale production systems.
Strong collaborator capable of partnering deeply with cross-functional teams (Product, Engineering, Business) to define problems and drive ML impact aligned with KPIs.
Technically proficient in ML platform architecture, experimentation frameworks, and cloud environments, with experience in building reusable, scalable ML solutions and frameworks across teams.