Description

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

Responsibilities

• Design data pipelines and engineering infrastructure to support large-scale machine learning systems.
• Develop and deploy scalable tools and services for machine learning training and inference.
• Support model development with a focus on auditability, versioning, and data security.
• Build Terraform modules to deploy standard infrastructure.
• Automate cloud operations using Python and Boto3.
• Create Docker images using custom Python libraries and deploy applications via containers.

Required Skills

• 10+ years of professional experience.
• AWS system and network architecture design, specifically AWS SageMaker and AWS ECS.
• Proficiency with AWS services including S3, DynamoDB, Glue, Redshift, RDS, Lambda, and API Gateway.
• Experience with Kubernetes and container orchestration in cloud environments.
• Hands-on experience with workflow orchestration frameworks like Kubeflow, Airflow, or Argo.
• Ability to develop Jenkins pipelines using Groovy scripting and XL Release.
• Strong Python development skills and experience with PyTest.
• Experience with AWS Step Functions and AWS workflow creation.
• Proficiency with Git, version control, and CI/CD practices.
• Knowledge of data security and privacy solutions such as Denodo, Protegrity, and synthetic data generation.

Preferred Skills

• Experience documenting technical solutions and creating solution diagrams.

Key Skills

Education

Any Graduate