AI/ML Automation Analyst

About the Role The AI/ML Support Automation Analyst will be a key member of the KSL AI Support Team, focusing on MLOps infrastructure, container orchestration, and workflow automation at a supercomputing scale. This role is responsible for developing and maintaining secure, OCI-compliant container images, robust CI/CD pipelines, and cloud-native MLOps workflows that enable researchers to efficiently deploy and manage AI/ML workloads. The Analyst will bridge the gap between cutting-edge Kubernetes-based infrastructure and the diverse needs of the research community, contributing to governance, technical enablement, and community development initiatives. Responsibilities MLOps and Container Development - Providing timely and useful user support via telephone, walk-in, email, and ticketing system submissions for all types of inquiries. - Maintain high customer service standards in dealing with and responding to user issues and questions. - Develop and maintain secure, OCI-compliant, and HPC-ready AI/ML and data science software container images. - Design and implement robust MLOps workflows and pipelines at supercomputing scale. - Develop and maintain CI/CD pipelines for reproducible infrastructure and workflow deployment. - Design and deploy APIs for AI/ML services and inference endpoints. - Implement and manage Kubernetes-based orchestration, including CNI, CSI, and service mesh configurations and optimization. - Deploy and maintain container registries (Harbor) and model registries (MLFlow, Kubeflow Model Registry). Governance and Compliance Support - Assist in computational readiness reviews for AI research projects. - Assist in AI model and artifact control reviews to ensure compliance with institutional standards. - Provide consultation to users on efficient resource usage for AI/ML and MLOps workflows. - Ensure container images and workflows comply with security policies and best practices. - Support the implementation of usage monitoring and reporting systems. Performance and Benchmarking - Perform performance debugging and tuning of MLOps and cloud-native workflows. - Develop and maintain AI/ML and MLOps workload benchmarks for procuring new systems. - Create and maintain regression testing workloads for existing clusters. - Deploy and maintain observability and resource monitoring stacks using Prometheus, Grafana, NVIDIA DCGM, and Grafana Loki. - Contribute to technology evaluation and benchmarking exercises for future infrastructure investments. Training and Documentation - Create comprehensive training content for users on MLOps platforms, Kubernetes, and containerization. - Develop and maintain high-quality user documentation for automation tools and workflows. - Support the delivery of workshops on CI/CD, container orchestration, and MLOps best practices. - Contribute to knowledge transfer initiatives within the KAUST research community. - Provide one-on-one consultation to researchers on efficient use of automation infrastructure. Qualifications - Bachelor's or master’s degree in computer science, Data Science, Computational Science, Artificial Intelligence, or a related field. - Certifications such as CKA (Certified Kubernetes Administrator), CKAD (Certified Kubernetes Application Developer), CKS (Certified Kubernetes Security Specialist), or CNPE (Certified Cloud Native Platform Engineer) are highly valued. Required Skills - Demonstrated experience developing robust and complex MLOps pipelines. - Hands-on experience with API design and deployment. - Experience developing robust and portable CI/CD pipelines for reproducible infrastructure and workflow deployment. - Experience supporting researchers or working in academic/research computing settings preferred. Technical Skills - Essential - Kubernetes: Strong expertise in Kubernetes, Container Network Interface (CNI), Container Storage Interface (CSI), and Service Mesh. - MLOps: Experience developing and maintaining MLOps pipelines and workflows. - CI/CD: Proficiency in building CI/CD pipelines for infrastructure and application deployment. - Containerization: Experience building secure, OCI-compliant container images. - API Development: Experience in API design, development, and deployment. - Programming: Proficiency in Python; experience with Go, Bash scripting. - Linux: Strong Linux/Unix systems administration skills. Technical Skills - Desired - Experience with ArgoCD, Airflow, DASK, Spark for workflow orchestration. - Experience with Kubeflow, KServe, and Seldon for ML serving and pipelines. - Experience deploying and maintaining observability stacks (Prometheus, Grafana, NVIDIA DCGM, Grafana Loki). - Knowledge of Model Context Protocol (MCP) and agentic frameworks. - Experience deploying inference services at scale. - Experience deploying and maintaining container registries (Harbor) and model registries (MLFlow, Kubeflow Model Registry, Artifact Hub). - Experience with GitOps practices and Infrastructure as Code (Terraform, Ansible). - Experience with HPC schedulers (SLURM) and HPC-cloud integration.