AI-ML Support Analyst

About the RoleThe 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.ResponsibilitiesGenerative AI Development and ConsultingProviding 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 SupportConduct 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 AssuranceDevelop 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 DocumentationCreate 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.QualificationsBachelor'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 SkillsTechnical Skills - EssentialProgramming: 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 - DesiredExperience 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 SkillsStrong 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.