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Tier-1 brand plus metro location balanced by specialized MLOps and seniority, creating moderate competition.
MLOps and cloud platform skills transfer across industries, though financial-domain experience is preferred.
Explicit 12+ years requirement plus mandatory cloud, Kubernetes, IaC, CI/CD, and MLOps skills increases shortlisting strictness.
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Design, build, and maintain scalable, secure cloud infrastructure specifically for AI/ML workloads, including Kubernetes-based container orchestration and MLOps workflows.
Develop and manage automated CI/CD pipelines for deploying AI applications and machine learning models, optimizing for performance, scalability, and cost efficiency.
Ensure high platform availability, implement security best practices including secrets management and IAM, troubleshoot production issues, and collaborate across technical teams to support AI/ML operations.
8–12+ years of experience with DevOps engineering and AI/ML platform infrastructure.
Strong expertise in at least one major cloud platform: AWS, Azure, or GCP.
Proficiency in Kubernetes, Docker, Infrastructure as Code tools (Terraform, Ansible), and CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD).
Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field (MS preferred).
Experienced in delivering complex, production-grade AI/ML platforms with demonstrable automation and operational excellence.
Skilled in MLOps frameworks (MLflow, Kubeflow), GPU-based training environments, and integrating AI services including familiarity with LLMs and generative AI technologies.
Capable of cross-functional collaboration with data scientists, ML/Software engineers, and platform teams, with strong background in cloud-native microservices architecture and monitoring/security tooling.