Google Cloud Machine Learning Services(AI Infrastructure Architect)

Cerebra; Bengaluru; On-site; INR 400,000 - 700,000; Full time

Job summary

Cerebra in Bengaluru seeks an AI Infrastructure Architect to design and build custom AI infrastructure and hardware solutions, optimizing performance, power, cost and scalability of the computational stack.

You will build and maintain ML pipelines for training, evaluation and deployment, and collaborate with data scientists to deploy and operationalize models, while ensuring monitoring and CI/CD for ML workflows.

Qualifications

• Minimum 5 years of experience in AI infrastructure or related field.
• Experience architecting and building AI infrastructure/hardware solutions.
• Familiarity with vendor evaluation and full stack integration is a plus.

Responsibilities

• Build and maintain ML pipelines for training, evaluation, and deployment
• Support model versioning, packaging, and CI/CD for ML workflows
• Monitor deployed models for performance, drift, and reliability
• Automate data preprocessing and feature pipelines
• Collaborate with data scientists to deploy and operationalize models
• Troubleshoot infrastructure and pipeline issues
• Maintain documentation of ML infrastructure and workflows
• Support scaling of training/inference infrastructure

Skills

Google Cloud ML Services

Education

15 years full time education

Job description

Project Role : AI Infrastructure Architect

Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.

Must have skills : Google Cloud Machine Learning Services

Good to have skills : Machine Learning Operations

Minimum 5 year(s) of experience is required

Educational Qualification : 15 years full time education

Summary:

Build and maintain ML pipelines for training, evaluation, and deployment

Roles & Responsibilities:

• - Build and maintain ML pipelines for training, evaluation, and deployment
• - Support model versioning, packaging, and CI/CD for ML workflows
• - Monitor deployed models for performance, drift, and reliability
• - Automate data preprocessing and feature pipelines
• - Collaborate with data scientists to deploy and operationalize models
• - Troubleshoot infrastructure and pipeline issues
• - Maintain documentation of ML infrastructure and workflows

Professional & Technical Skills:

• - Support scaling of training/inference infrastructure