Infrastructure & DevOps Engineer - AWS SageMaker Unified Studio

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Location: Remote (India / UK) Experience: 5-7 years Employment Type: Full-time About the Role We are seeking a skilled Infrastructure & DevOps Engineer to support the development and deployment of AWS SageMaker Unified Studio , building on the existing SageMaker ecosystem.
The role involves designing, automating, and maintaining cloud-native infrastructure that enables scalable, secure, and efficient machine learning workflows.
You will collaborate with data scientists, ML engineers, and platform teams to ensure seamless integration of new features into Unified Studio, focusing on reliability, automation, and operational excellence.
Key Responsibilities Design and implement cloud infrastructure to support SageMaker Unified Studio features.
Automate deployment pipelines using CI/CD tools (CodePipeline, Jenkins, GitHub Actions, etc.
). Manage infrastructure as code (IaC) with Terraform/CloudFormation.
Ensure scalability, security, and compliance of ML workloads in AWS.
Monitor and optimize SageMaker Studio environments , including notebooks, pipelines, and endpoints.
Collaborate with ML engineers to integrate new Unified Studio capabilities into existing workflows.
Implement observability solutions (CloudWatch, Prometheus, Grafana) for proactive monitoring.
Troubleshoot infrastructure and deployment issues across distributed ML systems.
Drive DevOps best practices for automation, testing, and release management.
Qualifications Bachelor’s degree in Computer Science, Engineering, or related field.
3–5 years of experience in Infrastructure/DevOps roles.
Strong expertise in AWS services : SageMaker, EC2, S3, IAM, CloudFormation, Lambda, EKS.
Hands-on experience with CI/CD pipelines and automation frameworks.
Proficiency in Terraform, CloudFormation, or Ansible for IaC.
Solid understanding of Docker & Kubernetes for containerized ML workloads.
Familiarity with ML workflows and SageMaker Studio (preferred).
Strong scripting skills in Python, Bash, or Go .
Experience with monitoring/logging tools (CloudWatch, ELK, Prometheus).
Excellent problem-solving and communication skills.
Preferred Skills Exposure to SageMaker Unified Studio or similar ML orchestration platforms.
Knowledge of data engineering pipelines and ML lifecycle management.
Experience working in financial services or regulated industries (bonus).