Machine learning ops

Job Description & Key Requirements

• 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.