- Role: MLOps Specialist / Senior MLOps Engineer
- Work Mode: Hybrid
- Experience: 5+ Years in DevOps / Cloud / MLOps
Core Technical Stack &
- Responsibilities:
- ML Lifecycle &
- Platforms:
End-to-end training and inference pipelines, model deployment, monitoring (data/concept drift detection), automated retraining, and Feature Store management using AWS SageMaker, MLflow, Kubeflow, or Databricks.
- Cloud &
- Infrastructure: Hands-on AWS infrastructure automation (SageMaker, Lambda, S3, ECS, IAM, RDS) and container orchestration using Docker, Kubernetes / EKS.
- Infrastructure as Code (IaC): Solid hands-on provisioning using Terraform and CloudFormation (CFT).
- CI/CD &
- Scripting: Building and automating robust CI/CD workflows using Python, Jenkins, and Git.