AWS,Terraform_India

Detailed JD (Roles and Responsibilities)

Candidate with Total 12+ years experience with below details:

• Mandatory Technical Skill : AWS Admin , Terraform , CI/CD
• Cloud DevOps Expertise: 5+ years designing and managing automated DevOps workflows for large-scale cloud environments.
• AWS Services Mastery: Hands-on experience with EC2, EKS, S3, VPC, IAM, CloudFormation.
• Infrastructure as Code (IaC): Advanced proficiency in Terraform for multi-stack deployments using modular design and best practices.
• CI/CD Automation: Proven experience building end-to-end automated release management platforms for IaC and Databricks pipelines using GitHub Actions, Jenkins, AWS CodePipeline.
• Integrated CI/CD Components:
  • Code Quality & Security Scanning: Tools like SonarQube or similar.
  • Automated Testing Frameworks: For Databricks notebooks and workflows.
  • Approval Workflows: Integrated gates for compliance and governance.
  • Integration with ITSM & Project Tools: ServiceNow, Jira for change management and ticketing.
  • Release Management Platforms: Hands-on experience with tools like Harness, ArgoCD, or similar for deployment orchestration.
• Audit Logging & Dashboards: Ability to set up audit logs, dashboards, and capture CI/CD metrics for compliance and performance monitoring.
• Infrastructure Planning & Scaling: Experience in planning and scaling infrastructure behind CI/CD pipelines for high availability and performance.
• Databricks Architecture & Configuration: Strong experience with Databricks Lakehouse Platform, including Unity Catalog and Delta Lake.
• Kubernetes Expertise: Ability to design and operate robust, scalable EKS clusters.
• Observability & Monitoring: Expertise in CloudWatch, Prometheus, ELK stack.
• Disaster Recovery & High Availability: Experience implementing DR and HA strategies.

1. • Should Have

• Databricks AI Features: Familiarity with Model Serving, Feature Store, MLflow.
• AWS AI/ML Services: SageMaker, Bedrock.
• DevOps for AI Agents & Models: Experience automating ML model lifecycle, including training, deployment, monitoring, and rollback strategies for AI agents and models.
• Cost Optimization: AWS pricing strategies.
• Serverless Architectures: Lambda, API Gateway.
• Platform as a Service (PaaS): Knowledge of AWS PaaS offerings (Elastic Beanstalk, Fargate) and integration patterns.

1. • Nice to Have

• AWS DevOps Engineer Professional Certification.
• Containerization: ECS, EKS, Docker.
• Terraform Certification.
• Strong communication skills.

Total Experience

12+ years