AI-ML Support Analyst

About the Role

Join the KAUST Supercomputing Lab (KSL) as an AI-ML Support Analyst and play a pivotal role in advancing cutting-edge artificial intelligence and machine learning research. As a member of the AI/ML Support Team, you will collaborate closely with the Team Lead to deliver high-impact AI research services to KAUST’s diverse research community. This position is designed for professionals who thrive at the intersection of AI innovation and computational infrastructure, bridging the gap between advanced technology and the unique needs of researchers across Climate & Weather, Bioinformatics, Computational Fluid Dynamics (CFD), Natural Language Processing (NLP), and multimodal AI. Your contributions will extend beyond technical support to include governance, technical enablement, and community development, ensuring researchers have the tools and expertise required to achieve their ambitious goals.

Key Responsibilities

The AI-ML Support Analyst will be responsible for a broad range of technical and consultative duties, including:

• Generative AI Development and Consulting:
  • Provide timely and high-quality user support through multiple channels, including telephone, in-person consultations, email, and ticketing systems, ensuring researchers receive prompt and effective assistance.
  • Develop and consult on large-scale Generative AI model training using domain-specific datasets across diverse research areas such as Climate & Weather, Bioinformatics, CFD, NLP, and multimodal AI.
  • Support researchers in fine-tuning foundation models on specialized datasets using advanced optimization techniques to enhance model performance and accuracy.
  • Design and implement robust data engineering pipelines to streamline AI research workflows and improve data accessibility and usability.
  • Develop and optimize AI workflows tailored to KSL’s high-performance computing (HPC) environment, maximizing efficiency and resource utilization.
  • Build and maintain secure, Open Container Initiative (OCI)-compliant, HPC-ready container images using tools such as Singularity, Podman, or equivalent technologies to ensure seamless deployment and scalability.
  • Design and implement complex workflows using SLURM and Kubernetes to facilitate distributed training and inference, enabling researchers to leverage KSL’s computational resources effectively.
• Governance and Compliance Support:
  • Conduct comprehensive computational readiness reviews for AI research projects to assess feasibility, resource requirements, and compliance with institutional standards.
  • Assist in AI model and artifact control reviews to ensure adherence to KAUST’s security policies and best practices, mitigating risks associated with data handling and model deployment.
  • Provide expert consultation to researchers on designing secure, compliant, and high-performance workflows that align with institutional policies and ethical guidelines.
  • Support the implementation of usage monitoring and reporting systems to track AI resource consumption, identify optimization opportunities, and ensure equitable access across projects.
  • Ensure all user workflows comply with KSL’s security policies and industry best practices, fostering a secure and reliable research environment.
• Benchmarking and Quality Assurance:
  • Develop and maintain computational benchmarks for AI workloads on KSL systems to evaluate performance, identify bottlenecks, and guide infrastructure improvements.
  • Create and maintain regression testing workloads to stress test system functionality, ensuring stability and reliability for critical research applications.
  • Support performance debugging and optimization activities for research workloads, collaborating with researchers to enhance computational efficiency and output quality.
  • Contribute to technology evaluation and benchmarking exercises for future infrastructure investments, providing data-driven insights to inform strategic decisions.
  • Perform benchmarking of new hardware and software configurations to assess their suitability for AI workloads, ensuring KSL remains at the forefront of computational innovation.
• Training and Documentation:
  • Develop comprehensive training materials and resources for end-users on KSL’s HPC systems, focusing on AI workloads, tools, and best practices to empower researchers with the knowledge they need to succeed.
  • Create and maintain high-quality technical documentation, including user guides, tutorials, and troubleshooting resources, to facilitate self-service support and reduce dependency on direct assistance.
  • Support the delivery of workshops and training sessions on distributed training, fine-tuning, and inference optimization, fostering a culture of continuous learning within the KAUST research community.
  • Contribute to knowledge transfer initiatives by sharing expertise, insights, and best practices with colleagues and researchers, enhancing the collective capabilities of the AI/ML support team.
  • Provide one-on-one consultations to researchers on the efficient use of computational resources, helping them optimize their workflows and achieve their research objectives more effectively.

Qualifications and Skills

To excel in this role, candidates must meet the following requirements:

• Education: Bachelor’s or master’s degree in Computer Science, Data Science, Computational Science, Artificial Intelligence, or a closely related field.
• Technical Skills – Essential:
  • Programming: Proficiency in Python; experience with R, Julia, Rust, or C/C++ is considered a strong asset.
  • AI/ML Frameworks: In-depth expertise in PyTorch and/or TensorFlow, with familiarity in JAX or similar frameworks.
  • Generative AI: Hands-on experience with foundation model development and fine-tuning techniques, including large language models and multimodal systems.
  • HPC Systems: Demonstrated experience in developing complex workflows using SLURM and/or Kubernetes for distributed computing environments.
  • Containerization: Proven ability to build efficient, HPC-ready container images using Singularity, Podman, or similar technologies, ensuring compatibility with KSL’s infrastructure.
  • Data Engineering: Experience with data engineering techniques, including pipeline development, data preprocessing, and workflow automation, to support AI research initiatives.
• Soft Skills:
  • Exceptional problem-solving abilities with a strong analytical mindset to diagnose and resolve complex technical issues.
  • Excellent communication and interpersonal skills to effectively collaborate with researchers, technical teams, and stakeholders across diverse domains.
  • Demonstrated ability to work independently and as part of a team in a fast-paced, dynamic research environment.
  • Commitment to continuous learning and staying updated with the latest advancements in AI, machine learning, and high-performance computing.