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Strong employer brand and metro, mid-level role, but niche ML platform skills reduce applicant density.
Cloud and MLOps skills transferable across industries, but ML platform specialization raises domain sensitivity.
Explicit 5–8 years and mandatory cloud, MLOps, Terraform, Kubernetes skills required.
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Lead technical architecture and development of enterprise-wide machine learning platform components including training infrastructure, model registries, feature stores, and deployment systems.
Drive scalability, reliability, and automation of ML infrastructure using cloud-native technologies (AWS, Azure, GCP), Kubernetes, Terraform, CI/CD, and MLOps pipelines.
Collaborate with cross-functional teams (data science, engineering, security, product) to enable scalable, compliant, and cost-optimized ML platform adoption.
5 or more years of relevant experience in cloud technologies, machine learning platforms, DevOps, and infrastructure automation.
Proficient with AWS, Azure, or GCP cloud platforms, Kubernetes, Terraform, and CI/CD tools relevant to ML operations.
Bachelor’s degree or equivalent combination of education and experience.
Work location in Bangalore or Pune; hybrid work model; shift timing 1 PM to 9:30 PM.
Experienced technologist with comprehensive hands-on expertise in ML platform architecture and cloud-native infrastructure at enterprise scale.
Strong leadership in driving platform strategy, architectural choices, and operationalizing MLOps with automation and governance focus.
Comfortable working in collaborative, cross-disciplinary environments involving data science, security, and product teams to translate requirements into scalable engineering solutions.