Description

You will build, deploy, and monitor machine learning pipelines using SageMaker, Databricks, and AWS Lambda. You own the full lifecycle from training to inference, ensuring reliability and scalability.

This role is on-site.

Responsibilities

• Build and operate ML training and inference pipelines on SageMaker, Databricks, and Lambda.
• Implement CI/CD for ML workflows, including model deployment, monitoring, drift detection, and retraining.
• Manage infrastructure using Terraform and CloudFormation, deploying containers via Docker and Kubernetes/EKS.
• Automate operations with Python scripts and Jenkins pipelines.
• Maintain feature stores and ensure robust model serving and monitoring.

Required Skills

• 5+ years of DevOps, Cloud, or MLOps experience.
• Strong proficiency in Python for scripting and automation.
• Hands-on experience with AWS services: SageMaker, Lambda, S3, ECS, IAM, RDS.
• Infrastructure as Code: Terraform and CloudFormation (CFT).
• Containerization and orchestration: Docker, Kubernetes, EKS.
• CI/CD tools: Jenkins, Git.
• MLOps platforms: MLflow, Kubeflow, Feature Stores.

Preferred Skills

• Experience with Databricks MLflow and Kubeflow.
• Strong troubleshooting skills for EKS and AWS infrastructure.

Key Skills

Education

Bachelor's degree