Description

You will design and manage the engineering infrastructure and data pipelines required to support enterprise machine learning systems at scale.

This role is hybrid.

Responsibilities

• Design and deploy scalable infrastructure and data pipelines for large-scale machine learning training and inference.
• Build Terraform modules to standardize and deploy cloud infrastructure.
• Automate cloud operations using Python and Boto3, and create Docker images with custom libraries.
• Support model development by ensuring auditability, versioning, and data security across the lifecycle.
• Develop Jenkins pipelines using Groovy scripting and manage releases via XL Release.

Required Skills

• 10+ years of professional experience in engineering or DevOps roles.
• Deep expertise in AWS architecture, specifically SageMaker, ECS, S3, DynamoDB, Glue, Redshift, RDS, Lambda, and API Gateway.
• Hands-on experience with Kubernetes and container orchestration in cloud environments.
• Proficiency with workflow orchestration frameworks such as Kubeflow, Airflow, or Argo.
• Strong Python development skills, including PyTest and AWS Step Functions.
• Experience with Git, version control, and CI/CD best practices.
• Knowledge of data security and privacy solutions like Denodo, Protegrity, and synthetic data generation.

Preferred Skills

• Experience documenting technical solutions and creating solution diagrams.

Key Skills

Education

Any Graduate