Key Responsibilities
- Design and manage AWS infrastructure (VPC, EC2, ECS/EKS, S3, IAM, Lambda, Step Functions, CloudFormation/Terraform).
- Build and maintain CI/CD pipelines for AI/ML models, data pipelines, and applications.
- Implement MLOps practices (versioning, automated training, testing, deployment, and monitoring of ML models).
- Work with AWS SageMaker for model training, deployment, monitoring, and drift detection.
- Automate data and ML workflows using tools like Airflow, Step Functions, or Kubeflow.
- Enable observability and monitoring with CloudWatch, Prometheus, Grafana, or similar tools.
Required Skills & Qualifications
- 4–8 years of experience in DevOps, Cloud Engineering, or MLOps (depending on seniority).
- Strong expertise in AWS services (SageMaker, S3, EKS/ECS, Lambda, API Gateway, Redshift, DynamoDB, Step Functions).
- Hands-on experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD, AWS CodePipeline).
- Proficiency with IaC tools (Terraform, AWS CDK, or CloudFormation).
- Strong skills in Python, Bash, or Go scripting.
- Familiarity with Docker, Kubernetes, and container orchestration.
- Knowledge of ML lifecycle management (data ingestion, model training, deployment, monitoring, retraining).
- Experience with monitoring and logging frameworks (CloudWatch, ELK, Datadog, Prometheus).
- Understanding of GitOps practices for managing infrastructure and ML models.
Job Types: Full-time, Permanent
Benefits:
- Flexible schedule
- Health insurance
- Provident Fund
- Work from home
Application Question(s):
- Do you have hands-on expertise in AWS SageMaker, ECS/EKS, Lambda, Step Functions?
- Do you have experience with Infrastructure as Code (Terraform/CloudFormation)?
Experience:
- DevOps: 4 years (Required)
Work Location: Remote
