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Tier-1 brand and metro Hyderabad increase interest, but senior specialized ML leadership reduces applicant density.
Requires deep ML engineering, MLOps and DS expertise, making cross-industry transferability limited.
Explicit 12–15 years plus leadership and mandatory ML platform/cloud production experience create high shortlisting strictness.
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Lead and manage a team of Machine Learning Engineers and Data Scientists delivering large-scale ML systems for critical business capabilities like user behavior modeling, fraud detection, and transaction intelligence.
Own end-to-end ML projects from problem definition to production, ensuring timelines, quality, and measurable business impact are met.
Develop and maintain ML platform capabilities including pipeline orchestration, experimentation frameworks, model deployment, and observability for scalable, reliable production ML systems.
12–15 years total experience with at least 3 years leading/managing ML and Data Science teams.
Proven experience designing and delivering large-scale, production ML systems and end-to-end ML platforms including pipeline orchestration, experiment tracking, model promotion, and observability.
Hands-on experience with cloud platforms (AWS, GCP, or Azure) and modern ML/data ecosystems.
Experience translating business problems into scalable ML/AI solutions with measurable impact.
Technically strong leader with hands-on expertise in ML engineering, platform architecture, and production-grade systems.
Effective cross-functional collaborator with experience working closely with Product, Engineering, Data, and Business teams to align ML solutions with KPIs.
Experienced in scaling teams, establishing quality standards, and driving operational excellence in ML lifecycle and platform capabilities.