AI-ML Support Analyst

About the Role

The AI/ML Support Analyst will be a key member of the KAUST Supercomputing Lab's (KSL) AI/ML Support Team, supporting the delivery of AI research services to KAUST's diverse research community. Working under the AI/ML Support Team Lead, this role focuses on developing and optimizing Generative AI models, maintaining computational benchmarks, and providing expert consultation to researchers across multiple scientific domains, including Climate & Weather, Bioinformatics, CFD, NLP, and multimodal AI. The analyst will help bridge the gap between cutting-edge computational infrastructure and the diverse needs of the research community, contributing to governance, technical enablement, and community development initiatives.

Responsibilities

Generative AI Development and Consulting

• 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 consult on large-scale Generative AI model training on domain-specific datasets across research areas including Climate & Weather, Bioinformatics, Computational Fluid Dynamics (CFD), NLP, and multimodal AI.
• Support researchers in fine-tuning foundation models on domain-specific datasets using advanced optimization techniques.
• Develop data engineering pipelines to support AI research workflows.
• Design and implement efficient AI workflows optimized for KSL's high-performance computing environment.
• Build and maintain secure, OCI-compliant, HPC-ready container images using Singularity, Podman, or similar.
• Develop complex workflows using SLURM and Kubernetes for distributed training and inference.

Governance and Compliance Support

• Conduct computational readiness reviews for AI research projects.
• Assist in AI model and artifact control reviews to ensure compliance with institutional standards.
• Support researchers in designing secure, compliant, and performant workflows.
• Provide expert consultation to researchers on efficient utilization of AI resources and best practices.
• Support the implementation of usage monitoring and reporting systems.
• Ensure user workflows comply with KSL security policies and best practices.

Benchmarking and Quality Assurance

• Develop and maintain computational benchmarks for AI workloads on KSL systems.
• Create and maintain regression testing workloads to stress test system functionality.
• Support performance debugging and optimization activities for research workloads.
• Contribute to technology evaluation and benchmarking exercises for future infrastructure investments.
• Perform benchmarking of new hardware and software configurations.

Training and Documentation

• Create comprehensive training materials for end-users on KSL's HPC systems hosting AI workloads and tools.
• Develop and maintain high-quality technical documentation.
• Support the delivery of workshops on distributed training, fine-tuning, and inference optimization.
• Contribute to knowledge transfer initiatives within the KAUST research community.
• Provide one-on-one consultation to researchers on efficient use of computational resources.

Qualifications

• Bachelor's or master's degree in computer science, Data Science, Computational Science, Artificial Intelligence, or a related field.
• Strong academic foundation in machine learning, deep learning, and AI fundamentals.

Required Skills

• Technical Skills - Essential
• Programming: Proficiency in Python; experience with R, Julia, Rust or C/C++ is a plus.
• AI/ML Frameworks: Strong expertise in PyTorch and/or TensorFlow, JAX or similar.
• Generative AI: Experience with foundation model development and fine-tuning techniques.
• HPC Systems: Experience developing complex workflows using SLURM and/or Kubernetes.
• Containerization: Experience building efficient HPC-ready container images using Singularity, Podman or similar.
• Data Engineering: Experience with data engineering techniques for developing AI pipelines.
• Linux: Strong Linux/Unix skills and bash scripting capabilities.

Technical Skills - Desired

• Experience with Cray EX supercomputers with NVIDIA GPUs.
• Experience with Kubeflow pipelines and Kubeflow Training Operator.
• Experience with distributed inference frameworks (NVIDIA Triton, NIM, SGLang, llama.cpp, llm-d, LLMcache).
• Knowledge of security vulnerability inspection in software libraries, AI models, datasets, and pipelines.
• Experience with software supply chain tools (JFrog, Nexus, Trivy, Cloudsmith).
• Experience with data management on S3-compatible object storage at scale.
• Experience with high-performance distributed filesystems (Lustre, Weka IO, VAST Data).
• Proficiency with NVIDIA Nsight and Compute for profiling AI workloads on GPUs.
• Experience developing CI/CD pipelines using GitLab, Travis, CircleCI, or similar tools.
• Experience with software build tools (autoconf, CMake, scons, SPACK, EasyBuild, Conda, Pip).

Soft Skills

• Strong problem-solving and analytical abilities.
• Excellent written and verbal communication skills in English.
• Customer service mindset with patience for supporting diverse skill levels.
• Ability to work independently and as part of a collaborative team.
• Strong documentation and knowledge-sharing practices.
• Cultural sensitivity for working in an international environment.