AI/ML Automation Analyst

KAUST (King Abdullah University of Science and Technology); Makkah Region; On-site; SAR 180,000 - 300,000; Full time

Job summary

KAUST is seeking an AI/ML Support Automation Analyst to advance MLOps and containerization for research workloads on HPC-scale infrastructure.

You will develop OCI-compliant images, robust CI/CD pipelines, and cloud-native workflows that empower researchers to deploy AI/ML models efficiently while conforming to security policies and governance. This role links Kubernetes infrastructure with KAUST's research community.

Qualifications

• Experience building robust MLOps pipelines for AI/ML workloads.
• Hands-on API design, deployment, and management experience.
• Proven ability to create reproducible CI/CD pipelines for infra and workflows.

Responsibilities

• Develop secure, OCI-compliant container images for AI/ML workloads.
• Design and operate scalable MLOps workflows and pipelines on HPC infrastructure.
• Build and maintain CI/CD pipelines for reproducible deployment.
• Develop APIs for AI/ML services and inference endpoints.
• Manage Kubernetes orchestration, CNI/CSI and service mesh tuning.

Skills

Kubernetes

MLOps

CI/CD

Containerization

API Development

Python

Go

Bash scripting

Education

Bachelor's or Master's in Computer Science/Data Science/AI

Tools

Harbor

MLFlow

Kubeflow

KServe

Seldon

ArgoCD

Airflow

Kubeflow Model Registry

Terraform

Ansible

SLURM

Job description

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.

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.