Infrastructure Engineer for ML Models

We are looking for a highly skilled Infrastructure Engineer to design and implement scalable and efficient infrastructure for machine learning models and AI applications.

Key Responsibilities

• Design and implement highly scalable infrastructure for machine learning models and AI applications.
• Develop and maintain infrastructure-as-code (IaC) using Terraform and configuration management tools such as Ansible and Puppet for automated provisioning and orchestration.
• Develop and maintain CI/CD pipelines to ensure seamless deployment and monitoring of machine learning models.
• Collaborate with data scientists and software engineers to integrate machine learning solutions into production environments.
• Automate routine tasks and workflows to improve efficiency and reduce manual intervention.
• Monitor and troubleshoot production systems to ensure high availability and performance.
• Implement security best practices to protect sensitive data and ensure compliance with industry standards.
• Identify and resolve infrastructure gaps to ensure reliable, efficient, and scalable solutions.
• Develop advanced AI/ML infrastructure solutions to enhance the efficiency of skilled ML teams.
• Deploy and manage open-source GenAI components, such as vector databases and various AI/ML models, ensuring seamless integration and optimal performance within the Kubernetes environment.

Requirements

• 4 to 7 years of hands-on experience as Infrastructure Engineer with a strong emphasis on Kubernetes knowledge.
• Experience in designing and implementing the scalable and efficient infrastructure with Kubernetes based automation.
• Experience in developing and maintaining CI/CD pipelines to ensure seamless deployment and monitoring of machine learning models.
• Experience to automate routine tasks and workflows to improve efficiency and reduce manual intervention.
• Experience to monitor and troubleshoot production systems to ensure high availability and performance.
• Experience to implement security best practices to protect sensitive data and ensure compliance with industry standards.
• Design and implement solutions for critical areas, including Distributed storage systems, Scheduling systems, High availability capabilities, Core reliability issues within large-scale GPU clusters.
• Knowledge of infrastructure as code (IaC) tools (e.g., Terraform, Ansible).
• Experience to implement and manage GPU infrastructure within Kubernetes clusters to support high-performance computing and AI/ML tasks, ensuring scalability and efficiency.