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.