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Tier-1 brand, mid-level ML role in Bangalore with broad MLOps skills increases competition.
Core MLOps and ML engineering skills are transferable, though advertising and bidding domain adds moderate domain specificity.
Explicit 5+ years and mandatory MLOps, Spark, and PyTorch/TensorFlow production requirements make shortlisting strict.
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Lead the development and operation of scalable ML pipelines for production, including batch and streaming inference workflows.
Monitor ML models in production to capture metrics (latency, accuracy, drift) and drive model iteration and issue diagnosis.
Design and implement guardrails and safety controls to protect business outcomes and reliability in production ML models.
5+ years of professional experience in end-to-end ML engineering pipelines including streaming applications in hybrid/cloud environments.
Bachelor’s or Master’s degree in a technical field (e.g., Computer Science) or equivalent experience.
Strong experience with Spark or similar big data frameworks, and ML libraries such as PyTorch and/or TensorFlow.
Experience with MLOps practices including CI/CD, experiment tracking, model registry and deployment, and monitoring ML models in production.
Experienced in big data and distributed system design focused on ML training and serving workflows.
Familiar with generative AI and LLMs including practical application of prompting, fine-tuning, embeddings, and responsible use in production.
Proficient in AI-assisted software and ML engineering tools (e.g., GitHub Copilot) to accelerate development and improve software quality.