The Data Scientist Architect will take the lead in designing and implementing advanced analytics solutions that drive data-driven decision-making across the organization. This role involves creating scalable data architectures, developing machine learning models, and collaborating closely with cross-functional teams to integrate analytics initiatives into business operations. You will play a critical role in architecting data solutions that support innovative analytics at DeepSource Technologies. Key Responsibilities: Design and oversee the construction of robust data architectures and pipelines to support extensive data analysis and modeling. Lead the development of machine learning algorithms and predictive models tailored to enhance business outcomes. Collaborate with data engineers and other stakeholders to ensure seamless data integration and optimization of data processing workflows. Conduct exploratory data analysis to uncover insights and trends that inform strategic business initiatives. Translate complex analytical results into actionable insights for non-technical stakeholders. Establish best practices for data governance, model validation, and testing in analytics. Stay updated with the latest advancements in data science, machine learning, and artificial intelligence to drive innovation. Mentor and guide junior data scientists and analytics teams to foster a culture of continuous learning and improvement. Document analytics processes, methodologies, and findings for internal reference and reporting purposes. Requirements: Ph.D. or Master's degree in Computer Science, Data Science, Statistics, or related field. 7+ years of experience in data science, machine learning, and analytics roles, with at least 3 years in an architectural or leadership position. Strong expertise in designing and implementing data architectures and workflows for large datasets. Proficiency in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Strong programming skills in Python and experience with SQL and databases (e.g., PostgreSQL, NoSQL). Experience in cloud environments and tools (AWS, Azure, or Google Cloud). Ability to communicate complex concepts clearly to a broad audience. Strong analytical and problem-solving skills with a focus on results. Published research papers or significant contributions to open-source projects in the field of data science are a plus. Benefits: Health Insurance Social Insurance
The Data Scientist Architect will take the lead in designing and implementing advanced analytics solutions that drive data-driven decision-making across the organization. This role involves creating scalable data architectures, developing machine learning models, and collaborating closely with cross-functional teams to integrate analytics initiatives into business operations. You will play a critical role in architecting data solutions that support innovative analytics at DeepSource Technologies. Key Responsibilities: Design and oversee the construction of robust data architectures and pipelines to support extensive data analysis and modeling. Lead the development of machine learning algorithms and predictive models tailored to enhance business outcomes. Collaborate with data engineers and other stakeholders to ensure seamless data integration and optimization of data processing workflows. Conduct exploratory data analysis to uncover insights and trends that inform strategic business initiatives. Translate complex analytical results into actionable insights for non-technical stakeholders. Establish best practices for data governance, model validation, and testing in analytics. Stay updated with the latest advancements in data science, machine learning, and artificial intelligence to drive innovation. Mentor and guide junior data scientists and analytics teams to foster a culture of continuous learning and improvement. Document analytics processes, methodologies, and findings for internal reference and reporting purposes. Requirements: Ph.D. or Master's degree in Computer Science, Data Science, Statistics, or related field. 7+ years of experience in data science, machine learning, and analytics roles, with at least 3 years in an architectural or leadership position. Strong expertise in designing and implementing data architectures and workflows for large datasets. Proficiency in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Strong programming skills in Python and experience with SQL and databases (e.g., PostgreSQL, NoSQL). Experience in cloud environments and tools (AWS, Azure, or Google Cloud). Ability to communicate complex concepts clearly to a broad audience. Strong analytical and problem-solving skills with a focus on results. Published research papers or significant contributions to open-source projects in the field of data science are a plus. Benefits: Health Insurance Social Insurance
We are looking for a skilled Senior Infrastructure & Virtualization Engineer to join our client’s team in Riyadh. The role involves managing and optimizing enterprise infrastructure, including virtualization platforms, storage, networking, and monitoring systems to ensure high performance, reliability, and scalability. Key Responsibilities: Manage and maintain VMware vSphere/vCenter environments, including ESXi hosts and virtual machines. Administer MariaDB databases for enterprise workloads. Deploy, configure, and maintain Dell PowerEdge XE servers, Dell PowerScale storage, and Dell PowerSwitch networking. Operate and manage Dell SFM (Server/Storage/Network Fabric Management) tools for monitoring and administration. Monitor infrastructure performance using Prometheus and Grafana, creating dashboards and alerts for proactive management. Collaborate with cross-functional teams to design, implement, and troubleshoot enterprise IT solutions. Ensure infrastructure security, compliance, and best practices for system and data availability. Requirements: Strong experience with VMware virtualization (vCenter, ESXi, cluster management). Experience with MariaDB or other relational databases. Hands-on experience with Dell PowerEdge XE, PowerScale, PowerSwitch, and SFM tools. Proficient in monitoring and alerting tools such as Prometheus and Grafana. Strong troubleshooting, performance tuning, and capacity planning skills. Solid understanding of networking, storage, and enterprise IT infrastructure.
Role Overview: We are seeking a highly skilled Senior AI Infrastructure & Platform Engineer to join our client’s team in Riyadh. In this role, you’ll be responsible for building, managing, and optimizing scalable AI infrastructure and compute environments that support high-performance workloads, including GPU-accelerated AI/ML pipelines, cluster scheduling, and orchestration. Key Responsibilities: Deploy, maintain, and optimize GPU-based compute clusters and infrastructure. Manage and operate GPU orchestration tools and platforms such as: Nvidia Base Command Manager (critical) Nvidia AI Enterprise Suite Nvidia GPU and Network Operators Nvidia NIMs and Blueprints Configure, deploy, and maintain compute workloads using scheduling and orchestration tools including: Slurm (critical) Vanilla Kubernetes Install, configure, and maintain the underlying OS (e.g. Canonical Ubuntu) and supporting system software. Monitor and troubleshoot infrastructure performance, availability, and reliability; ensure high uptime for AI/ML workloads. Work with data scientists, ML engineers, and dev teams to define infrastructure requirements, resource allocation, and deployment workflows. Develop automation scripts, CI/CD pipelines, and best practices for infrastructure provisioning and management. Document architecture, configurations, and operational procedures; enforce security, compliance, and backup policies. Requirements: Required Skills & Experience: Proven experience managing GPU-based AI/ML infrastructure and compute clusters. Hands-on experience with: Nvidia Base Command Manager Nvidia AI Enterprise Suite Nvidia GPU/Network Operators, NIMs, Blueprints Strong experience with Slurm and/or Kubernetes orchestration. Solid Linux system administration skills — preferably on Ubuntu or similar distributions. Strong scripting/automation ability (e.g. Bash, Python, or relevant tooling) for provisioning, deployment, and maintenance. Excellent troubleshooting and performance-tuning skills. Experience collaborating with ML/data science teams and integrating infrastructure with their workflows. Strong understanding of networking, security, resource allocation, and cluster management best practices. Preferred Qualifications: Previous experience working in a high-performance computing (HPC) or AI-focused infrastructure team. Knowledge of containerization, container orchestration, and GPUs in cloud or on-prem environments. Experience with CI/CD, infrastructure-as-code (e.g. Terraform, Ansible), monitoring tools, and logging setups. Familiarity with workload scheduling, job queuing, resource quotas, and GPU-shared environments.