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Tier-1 brand, mid-level requirement, and metro location create high candidate competition.
Specialized ML platform and cloud tooling require domain-specific platform experience, moderately limiting transferability.
Explicit 5+ and 3+ year requirements plus mandatory AWS, Terraform, Kubernetes increase strictness.
Design, build, automate, and support cloud infrastructure and deployment pipelines for a centralized Data Science and AI/ML platform on AWS.
Manage highly available, secure, and scalable AWS infrastructure using Infrastructure as Code including Terraform and Python automation.
Build and optimize CI/CD pipelines (GitLab or equivalent), containerize applications with Docker and Kubernetes, and promote DevOps and infrastructure standardization across teams.
5+ years of Python experience in automation, deployment utilities, infrastructure tooling, and operational automation.
3+ years of Terraform experience provisioning and managing AWS infrastructure, including reusable modules and remote state management.
Strong hands-on AWS experience including SageMaker, Bedrock, EC2, VPC, IAM, S3, RDS, EKS.
Experience with Docker containerization and Kubernetes cluster management, including Helm charts, networking, autoscaling, and rolling deployments.
Experienced in cloud-native AI/ML platform engineering with strong expertise in AWS services relevant to Data Science/ML workloads.
Skilled in end-to-end DevOps practices including CI/CD pipeline automation and Infrastructure as Code in a multi-team federated engineering environment.
Comfortable leading platform engineering efforts that emphasize automation, operational excellence, security, and scalability for financial market infrastructure.