Associate Data Scientist (Saudi Arabia)

We are looking for an experienced Associate Data Scientist with strong expertise in Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI), and Large Language Models (LLMs). The ideal candidate should have hands-on experience in AI model development, deployment, cloud infrastructure, distributed computing, and MLOps/LLMOps to support scalable, high-performance AI applications. Key Responsibilities • Develop, train, test, evaluate, and deploy AI/ML models and Generative AI applications. • Design, implement, and optimize LLM-based solutions, including inference mechanisms and performance tuning. • Build and manage scalable AI infrastructure using GCP, OpenShift, Kubernetes, and Docker. • Implement MLOps and LLMOps pipelines to automate model development, deployment, monitoring, and maintenance. • Optimize LLM inference performance through distributed computing, inference benchmarking, and scaling strategies to meet SLAs/SLOs. • Manage GPU-based workloads, distributed processing, and autoscaling for AI applications. • Work with NVIDIA NIM, Hugging Face, and NVIDIA SuperPOD environments, including HPC, Slurm, and Kubernetes. • Develop AI application architectures and manage end-to-end AI workflows. • Implement monitoring, dashboards, and performance tracking for ML/LLM workloads and applications. • Establish and maintain CI/CD pipelines using tools such as Argo CD, Git, and Jenkins. • Collaborate with cross-functional teams, manage project activities, and ensure the delivery of scalable AI solutions. Requirements • Bachelor's degree in Computer Science or a related field. • More than 5 years of relevant professional experience in Data Science, AI, Machine Learning, or related fields. • Strong programming skills in Python and SQL. • Hands-on experience with cloud technologies such as GCP, OpenShift, Kubernetes, and Docker. • Strong understanding of AI/ML model training, testing, evaluation, and deployment. • Practical knowledge of MLOps, LLMOps, LLMs, and Generative AI. • Understanding of LLM architecture, inference mechanics, and performance optimization. • Experience in inference scaling, distributed computing, benchmarking, and GPU-based workloads. • Familiarity with NVIDIA NIM, Hugging Face, and NVIDIA SuperPOD environments. • Experience with monitoring, dashboards, AI application architecture, and end-to-end workflows. • Knowledge of DevOps practices and CI/CD tools, including Argo CD, Git, and Jenkins. • Excellent communication, professionalism, leadership, and project management skills. Preferred Skills • Experience working with large-scale AI infrastructure and distributed AI workloads. • Strong analytical and problem-solving abilities. • Effective communication, teamwork, and stakeholder management skills. • Ability to lead project activities and collaborate across technical teams. Show more Show less

Bachelor's degree in Computer Science or a related field. More than 5 years of relevant professional experience in Data Science, AI, Machine Learning, or related fields. Strong programming skills in Python and SQL. Hands-on experience with cloud technologies such as GCP, OpenShift, Kubernetes, and Docker. Strong understanding of AI/ML model training, testing, evaluation, and deployment. Practical knowledge of MLOps, LLMOps, LLMs, and Generative AI. Understanding of LLM architecture, inference mechanics, and performance optimization. Experience in inference scaling, distributed computing, benchmarking, and GPU-based workloads. Familiarity with NVIDIA NIM, Hugging Face, and NVIDIA SuperPOD environments. Experience with monitoring, dashboards, AI application architecture, and end-to-end workflows. Knowledge of DevOps practices and CI/CD tools, including Argo CD, Git, and Jenkins. Excellent communication, professionalism, leadership, and project management skills.

Develop, train, test, evaluate, and deploy AI/ML models and Generative AI applications. Design, implement, and optimize LLM-based solutions, including inference mechanisms and performance tuning. Build and manage scalable AI infrastructure using GCP, OpenShift, Kubernetes, and Docker. Implement MLOps and LLMOps pipelines to automate model development, deployment, monitoring, and maintenance. Optimize LLM inference performance through distributed computing, inference benchmarking, and scaling strategies to meet SLAs/SLOs. Manage GPU-based workloads, distributed processing, and autoscaling for AI applications. Work with NVIDIA NIM, Hugging Face, and NVIDIA SuperPOD environments, including HPC, Slurm, and Kubernetes. Develop AI application architectures and manage end-to-end AI workflows. Implement monitoring, dashboards, and performance tracking for ML/LLM workloads and applications. Establish and maintain CI/CD pipelines using tools such as Argo CD, Git, and Jenkins. Collaborate with cross-functional teams, manage project activities, and ensure the delivery of scalable AI solutions.