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Associate Data Scientist (Saudi Arabia)

Eram Talent
Dhahran, KSA
Mid · 5+ years experience
Onsite
Discovered 3 weeks ago
argodockergcpgitjenkinskubernetes

Full Job Posting

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.

Preferred Qualifications

  • 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.

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.

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