Executive-KDNI

Executive-KDNI

As a DevOps professional in our team, you will play a pivotal role in designing, implementing, and managing the infrastructure and deployment pipelines that support our AI-driven applications. You will work closely with data scientists, AI researchers, and software engineers to ensure seamless integration and optimal performance of AI solutions.

Key responsibilities include:

Cloud Infrastructure Management:
• Design, deploy, and manage AI solutions on Azure and Google Cloud Platform (GCP).
• Optimize cloud resources to ensure cost-effectiveness and high performance.

CI/CD Pipeline Development:
• Develop and maintain continuous integration and continuous deployment pipelines for AI applications on Azure DevOps and GitHub Actions.
• Develop and maintain automated code and security scan pipelines.
• Automate deployment processes to streamline workflows and reduce time-to-market.

Hardware Integration:
• Manage and configure specialized hardware workstations including HP Fury Z8, Dell 7960 XCTO, and Nvidia GCX Studio A100.
• Ensure seamless integration between cloud services and on-premises hardware resources.

AI/ML Operations:
• Implement AI Ops, ML Ops, and RAG Ops practices to enhance the reliability and scalability of AI systems.
• Monitor system performance, troubleshoot issues, and implement improvements.

Collaboration and Support:
• Collaborate with cross-functional teams to understand requirements and deliver robust AI solutions.
• Provide technical support and guidance to team members regarding DevOps best practices and tools.

Security and Compliance:
• Ensure all deployments adhere to security standards and compliance regulations.
• Implement and maintain security protocols for both cloud and on-premises environments.

Educational Qualifications 
• Bachelor’s degree in Computer Science, Engineering, or a related field. Master’s degree preferred.

Work Experience
• 1-3 Years of Work Experience
• Strong expertise in Azure AI Studio and developing AI solutions on the Azure platform.
• Experience with Google Cloud Platform (GCP) in deploying and managing AI solutions.
• Proven experience as a DevOps Engineer, preferably within AI or machine learning environments.

Skills
• Proficiency with cloud services, including compute, storage, networking, and AI/ML tools on Azure and GCP.
• Hands-on experience with CI/CD tools such as Azure DevOps or GitHub Actions or Jenkins.
• Familiarity with containerization and orchestration technologies like Docker and Kubernetes.
• Knowledge of infrastructure as code (IaC) tools such as Terraform or Azure Resource Manager.

Experience Level

Executive Level