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Tier-1 brand and Bangalore metro boost applicant density, but senior ML/platform focus narrows candidate pool.
ML/MLOps and data platform skills are transferable, though logistics domain expertise is beneficial.
Requires proven data platform, MLOps, and leadership experience, creating stringent screening for candidates.
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Lead development and scaling of a global Data and AI platform supporting company-wide innovation.
Act as hands-on technical leader, owning architecture, coding key components, and solving complex problems alongside the team.
Design scalable shared data models, pipelines, and APIs; embed AI to improve data quality, automation, and ML Ops capabilities.
Strong experience in data engineering with large-scale data and AI platforms.
Proven leadership managing engineering teams while remaining technically hands-on (architecture and coding).
Hands-on experience with AI/ML systems in production including MLOps/AI Ops practices.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced engineering leader comfortable both managing teams and deep technical involvement.
Strong background in cloud-native data architectures and platform engineering best practices.
Demonstrated ability to infuse AI into platforms for automation, quality improvement, and scalable ML lifecycle management.