AI/ML Automation Analyst

About the Role

Join our dynamic team as an AI/ML Automation Analyst, a pivotal role within the KSL AI Support Team. This position is designed for professionals who thrive at the intersection of cutting-edge technology and research innovation, focusing on MLOps infrastructure, container orchestration, and workflow automation at an enterprise scale. As a key contributor, you will develop and maintain secure, OCI-compliant container images, robust CI/CD pipelines, and cloud-native MLOps workflows that empower researchers to seamlessly deploy and manage AI/ML workloads. This role bridges the gap between advanced Kubernetes-based infrastructure and the diverse needs of the research community, while contributing to governance, technical enablement, and community development initiatives.

Key Responsibilities

The AI/ML Automation Analyst will play a multifaceted role, encompassing the following core responsibilities:

MLOps and Container Development

• Deliver exceptional user support through multiple channels, including telephone, walk-in consultations, email, and ticketing systems, ensuring high standards of customer service in addressing user inquiries and technical challenges.
• Develop and maintain secure, OCI-compliant, and HPC-ready AI/ML and data science software container images tailored for supercomputing environments.
• Design and implement scalable MLOps workflows and pipelines optimized for high-performance computing (HPC) and supercomputing infrastructure.
• Create and maintain robust CI/CD pipelines to ensure reproducible infrastructure and workflow deployment, enhancing reliability and efficiency.
• Develop and deploy APIs for AI/ML services and inference endpoints, enabling seamless integration and scalability.
• Implement and optimize Kubernetes-based orchestration, including Container Network Interface (CNI), Container Storage Interface (CSI), and service mesh configurations to enhance performance and security.
• Deploy and maintain container registries (e.g., Harbor) and model registries (e.g., MLFlow, Kubeflow Model Registry) to streamline artifact management and collaboration.

Governance and Compliance Support

• Assist in computational readiness reviews for AI research projects, ensuring alignment with institutional standards and best practices.
• Support AI model and artifact control reviews to maintain compliance with security policies and institutional guidelines.
• Provide expert consultation to users on optimizing resource usage for AI/ML and MLOps workflows, promoting efficiency and cost-effectiveness.
• Ensure container images and workflows adhere to security policies, regulatory requirements, and industry best practices.
• Support the implementation of usage monitoring and reporting systems to track resource consumption and performance metrics.

Performance and Benchmarking

• Conduct performance debugging and tuning of MLOps and cloud-native workflows to enhance efficiency and reliability.
• Develop and maintain AI/ML and MLOps workload benchmarks to inform procurement decisions for new systems and infrastructure investments.
• Create and maintain regression testing workloads to validate the stability and performance of existing clusters.
• Deploy and maintain observability and resource monitoring stacks using tools such as Prometheus, Grafana, NVIDIA DCGM, and Grafana Loki to ensure proactive issue detection and resolution.
• Contribute to technology evaluation and benchmarking exercises to guide future infrastructure investments and strategic planning.

Training and Documentation

• Develop comprehensive training content and resources for users on MLOps platforms, Kubernetes, and containerization technologies.
• Create and maintain high-quality user documentation for automation tools, workflows, and best practices to facilitate self-service and reduce support overhead.
• Support the delivery of workshops and training sessions on CI/CD, container orchestration, and MLOps best practices to foster a skilled and informed research community.
• Contribute to knowledge transfer initiatives within the KAUST research community, ensuring alignment with institutional goals and fostering collaboration.
• Provide one-on-one consultations to researchers, offering tailored guidance on the efficient use of automation infrastructure and workflow optimization.

Qualifications and Skills

To excel in this role, candidates must meet the following qualifications and possess the required technical and interpersonal skills:

Education and Certifications

• Bachelor’s or master’s degree in Computer Science, Data Science, Computational Science, Artificial Intelligence, or a closely related field.
• Certifications such as Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), Certified Kubernetes Security Specialist (CKS), or Certified Cloud Native Platform Engineer (CNPE) are highly advantageous and demonstrate a commitment to professional excellence.

Technical Skills

• MLOps: Proven experience in developing and maintaining robust MLOps pipelines and workflows, with a focus on scalability and reproducibility.
• API Development: Hands-on experience in designing, developing, and deploying APIs for AI/ML services and inference endpoints.
• CI/CD: Proficiency in building and maintaining CI/CD pipelines for infrastructure and application deployment, ensuring seamless integration and deployment processes.
• Kubernetes: Strong expertise in Kubernetes, including Container Network Interface (CNI), Container Storage Interface (CSI), and service mesh configurations, with a focus on optimization and security.
• Containerization: Experience in developing secure, OCI-compliant container images optimized for HPC and supercomputing environments.
• Monitoring and Observability: Familiarity with tools such as Prometheus, Grafana, NVIDIA DCGM, and Grafana Loki for performance monitoring, debugging, and resource optimization.
• Research Computing: Prior experience supporting researchers or working in academic/research computing environments is preferred, with a deep understanding of the unique challenges and requirements of the research community.

Soft Skills

• Exceptional problem-solving abilities and a proactive approach to addressing technical challenges.
• Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and researchers.
• Commitment to continuous learning and staying abreast of emerging trends in AI/ML, MLOps, and cloud-native technologies.
• Ability to translate complex technical concepts into accessible training materials and documentation for diverse audiences.