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.

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