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Tier-1 brand and Bangalore metro increase competition, while senior ML manager specialization moderates applicant density.
High because ML productionization and platform leadership skills are specialized and less transferable across industries.
High due to senior managerial level, required ML productionization, infrastructure expertise, and cross-functional leadership.
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Owns the implementation and continuous improvement of machine learning model productionization processes across multiple teams, ensuring models meet organizational deployment readiness standards.
Manages and guides multiple medium- to large-scale ML engineering projects, setting priorities, aligning with business needs, and driving cross-functional collaboration to integrate ML models into systems.
Leads efforts to troubleshoot, debug, and improve ML infrastructure, workflows, and tools, while promoting automation, code quality, and integration of current ML field advancements.
Experience Required: Management of multiple medium- to large-scale machine learning projects or initiatives across teams.
Technical expertise with machine learning model productionization and deployment processes including scaling, automation (ETL to deployment), and continuous monitoring.
Familiarity with third-party ML frameworks like PyTorch, TensorFlow, or Keras for production environments.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in managing cross-functional machine learning engineering teams focusing on model deployment, production readiness, and operational stability.
Able to lead strategy and improvements across multiple teams on ML workflows, data quality, model performance evaluation, and tooling development.
Strong collaborating and coaching skills to drive team performance, continuous learning, knowledge sharing, and adoption of best practices in complex enterprise environments.