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Tier-1 brand, mid-level generalist ML role, Bengaluru location, and broad skillset increase competition.
Core ML engineering and MLOps skills transfer across industries, though ad/travel domain knowledge provides advantage.
Explicit 5+ years requirement plus mandatory production MLOps, Spark and PyTorch skills raise shortlisting strictness.
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Lead development and maintenance of scalable ML and data processing pipelines, focusing on production deployment and monitoring.
Collaborate cross-functionally to translate data science experiments into robust, efficient production workflows supporting online and batch inference.
Implement and improve ML model observability, diagnostics, retraining pipelines, and production safety guardrails to ensure quality and reliability.
5+ years professional experience with end-to-end ML engineering pipelines in production, including streaming applications.
Bachelor’s or Master’s degree in a technical field (e.g., Computer Science) or equivalent experience.
Proficiency with big data frameworks like Spark and ML libraries such as PyTorch or TensorFlow, including production model serving at scale.
Experience with ML model monitoring, MLOps practices (CI/CD, experiment tracking, model registry), and secure data access/governance.
Senior ML engineer accustomed to owning complex, high-scale production ML systems supporting big data environments.
Experienced in integrating generative AI/LLM techniques responsibly into ML lifecycle and familiar with AI-assisted software engineering tools.
Strong focus on operational reliability, observability, and iterative model improvement informed by production metrics and alerts.