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Tier-1 brand and broad cloud/MLOps skillset increase competition, moderated by senior 12+ years requirement.
Deep cloud, Kubernetes, IaC and MLOps expertise makes cross-industry transitions difficult, high domain sensitivity.
Explicit 12+ years and many mandatory cloud, Kubernetes, IaC and MLOps requirements create high filtering strictness.
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Design, build, and maintain scalable, secure cloud platforms for AI/ML workloads including automation of infrastructure provisioning and deployment pipelines.
Implement and manage Kubernetes containerized workloads and MLOps workflows covering training, validation, deployment, and monitoring of machine learning models.
Optimize cloud resources for performance, scalability, cost-efficiency; monitor platform health and troubleshoot production issues ensuring high availability and security compliance.
8–12+ years of experience in DevOps or Cloud Platform Engineering supporting AI/ML workloads.
Strong expertise in at least one major cloud platform: AWS, Azure, or GCP.
Proficiency in Kubernetes, Docker, Infrastructure as Code tools like Terraform and Ansible, and CI/CD tools such as Jenkins, GitHub Actions, or GitLab CI/CD.
Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field; Master's degree strongly preferred.
Experienced in enterprise application architecture and production delivery of complex AI/ML platforms within finance or similar regulated domains.
Skilled in MLOps frameworks (e.g., MLflow, Kubeflow) and managing GPU-based, distributed AI training environments.
Able to collaborate across cross-functional teams including data scientists, ML engineers, and developers to drive technical excellence in AI infrastructure and operations.