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.
📌 Machine learning ops (India)
🏢 Infosys
📍 India