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Tier-1 brand and Bangalore metro increase applicants, but senior niche ML platform requirements reduce candidate pool.
Role requires specialized ML platform and distributed-systems experience, limiting cross-industry transferability.
Explicit 10+ years and mandatory ML platform, cloud, and infra skills make shortlisting highly selective.
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Design and build a scalable Data and ML platform enabling ML experimentation pipelines for thousands of users.
Collaborate with data scientists and engineers to implement scalable ML pipelines for data preparation, feature engineering, model training, and deployment.
Drive software best practices, code reviews, and integrate cloud technologies (Kubernetes, blob storage) in a production-focused ML environment.
Bachelor's in Computer Science, Data Science, Statistics, or related field with 10+ years experience; OR Master's with 8+ years; OR Ph.D. with 6+ years experience.
3+ years production experience developing and deploying machine learning solutions.
Experience with ML platform tools (e.g. Jupyter, MLFlow, Ray, Vertex AI) and distributed computing/orchestration (Kubernetes, Airflow).
Expertise in Python, CI/CD (GitHub Actions), containerization, and cloud services; experience with Apache Spark/Flink or similar; experience with infrastructure-as-code tools (Terraform, FluxCD).
Technical operator experienced at the intersection of ML engineering and large-scale data platforms, capable of building tools that bridge data scientists and software engineering.
Comfortable working in cloud-first, production-grade distributed environments with extensive ML pipeline orchestration.
Demonstrated ability to modularize complex ML workflows into repeatable, scalable components and enforce software engineering best practices in data science teams.