Senior SRE - Kubernetes

Job Overview

Your Role

We are seeking a skilled Senior Site Reliability Engineer (Kubernetes) to support the deployment and maintenance of Generative AI applications. The ideal candidate will excel in setting up and maintaining CI/CD pipelines, implementing Infrastructure as Code (IaC), and managing cloud and OnPrem environments. This role involves collaborating closely with MLOps Engineers and Full-Stack Developers to ensure the seamless integration and delivery of AI-driven solutions, with a focus on MLOps workflows.

Design, Implement, and Maintain CI/CD Pipelines:

• Develop and manage robust CI/CD pipelines for application and AI model deployments.
• Automate testing, integration, and delivery processes to ensure smooth updates across environments.

MLOps Support and Automation:

• Collaborate with MLOps Engineers to deploy AI models from development to production.
• Implement model versioning, monitoring, and automated updating systems to maintain model efficiency.

Infrastructure as Code (IaC) Management:

• Build and maintain infrastructure using IaC tools such as Terraform, CloudFormation, or Ansible.
• Optimize infrastructure for scalability and reliability, ensuring cost-effective cloud and OnPrem solutions.

Cloud and OnPrem Environment Management:

• Manage and maintain cloud environments (AWS, Azure, GCP) for AI applications.
• Leverage OpenShift for OnPrem deployments and container orchestration.

Collaboration with Teams:

• Work closely with MLOps Engineers to streamline machine learning workflows, ensuring smooth transitions from model training to deployment.
• Partner with Full-Stack Developers to integrate AI models into applications, ensuring compatibility and high performance in production environments.

Monitoring and Optimization:

• Implement monitoring tools to track application and model performance.
• Continuously optimize deployments to improve reliability, scalability, and efficiency.

Your Skills and Experience  

Technical Skills:

• Strong expertise in containerization tools (Docker) and orchestration platforms (Kubernetes).
• Proficiency in CI/CD tools such as Jenkins, GitLab CI, CircleCI, or similar. Experience with Jenkins or GitLab CI is highly desirable.
• Familiarity with MLOps tools such as Kubeflow, MLflow, or Apache Airflow for managing AI model lifecycles.
• Practical knowledge of Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible.

AI/ML Knowledge:

• Understanding of AI and machine learning workflows, and their integration into deployment pipelines.
• Experience in supporting the deployment of Generative AI models is a strong plus.

OnPrem and Cloud Expertise:

• Experience managing cloud environments (AWS, Azure, GCP) and OnPrem solutions, specifically using OpenShift.

Soft Skills:

• Strong problem-solving and analytical skills with a focus on automation and efficiency.
• Excellent collaboration and communication skills to work effectively with cross-functional teams.

Certifications (Preferred):

• Kubernetes (CKA or CKAD).
• AWS, Azure, or GCP certifications.
• DevOps-related certifications (e.g., Certified Jenkins Engineer).