Full Stack Machine Learning Engineer (Datacentre AI Engineering) - Riyadh, KSA
- Riyadh, Saudi Arabia
Posted yesterday
About the role
Company:
Qualcomm Middle East Information Technology Company LLC
Job Area:
Engineering Group, Engineering Group > Software Engineering
General Summary:
About Us
Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including 5G-enabled smartphones that double as pro-level cameras and gaming devices, smarter vehicles and cities, and the technology behind the smart, connected factories that manufactured your latest purchase. Qualcomm 5G and AI innovations are the power behind the connected intelligent edge. You’ll find our technologies behind and inside the innovations that deliver significant value across multiple industries and to billions of people every day.
Qualcomm is growing its presence in Riyadh and is hiring Data Centre Engineers to support our expanding infrastructure across the region.
As Saudi Arabia accelerates its digital transformation under Vision 2030, Qualcomm is investing in world‑class computing and data centre capabilities to power AI, cloud, and advanced connectivity at scale. This is a unique opportunity to work in a fast‑growing technology hub, supporting critical environments and helping shape the future of data centre operations in the Kingdom and beyond.
About The Role
We are seeking a Full-Stack Machine Learning Engineer to join our team, bridging AI solutions development with AI platform engineering for Qualcomm’s AI Inference Suite and rack-scale data center deployments. This role involves designing, delivering, and supporting end-to-end AI services, agentic workflows, and fine-tuning pipelines, while enabling lifecycle automation, orchestration, and observability for large-scale data center environments.
You will bring a strong combination of full-stack engineering expertise, machine learning proficiency, and infrastructure knowledge to build robust, scalable AI systems.
Key Responsibilities Will Include:
- API Development & Optimization: Build and optimize API serving layers for AI inference workloads, ensuring model and hardware efficiency.
- Agentic Workflows & RAG Pipelines: Develop intelligent agents and retrieval-augmented generation workflows using frameworks such as LangChain and crew.ai.
- Model Lifecycle Management: Implement production-grade bring-your-own-model and fine-tuning flows, including dataset ingestion, orchestration, evaluation, and deployment.
- LLM Runtime Integration: Work with various LLM runtimes (e.g., vLLM, Dynamo, llm-d) and leverage inference optimization techniques.
- SDK & Tooling Contributions: Contribute to AI Inference Suite SDKs (Python/TypeScript/Java/Rust), CLI tools, and reference applications.
- Cluster Management: Design and maintain AI cluster management software for provisioning, orchestration, and monitoring.
- Telemetry & Observability: Integrate out-of-band management via Redfish/IPMI and in-band telemetry using Prometheus/OpenTelemetry.
- Infrastructure-as-Code: Develop workflows using MAAS, Terraform, and Ansible for bare-metal and containerized deployments.
- Kubernetes Orchestration: Enable Kubernetes/Helm-based orchestration for inference clusters and multi-tenancy.
- Monitoring & Dashboards: Build dashboards for rack health, inventory, and SLA compliance.
- Continuous Innovation: Stay current with GenAI trends, rack-scale AI orchestration, and data center best practices.
- Bachelor’s degree in Computer Science, Engineering, or related field.
- 5+ years of software engineering experience; 3+ years in ML or HPC environments.
- Strong programming skills in Python, Rust/Go, and TypeScript, with solid software development fundamentals.
- Deep understanding of data structures and algorithms in distributed systems and high-performance computing contexts.
- Hands-on experience with Kubernetes, Helm, Prometheus/OpenTelemetry, and Ansible/Terraform.
- Practical experience with LLM runtimes, agent frameworks, and rack-scale orchestration.
- Master’s degree in Computer Science, Machine Learning, or related field.
- Experience building inference and fine-tuning pipelines, as well as agentic workflows.
- Knowledge of data centre resource lifecycle management, out-of-band protocols (Redfish/IPMI), and MAAS/OpenStack.
- Exposure to scale-up data centre networking technologies (RoCE/RDMA/NVLink).
- Contributions to inference and GenAI model performance optimization.
Apart from working with great people, we offer the below:
- Salary including housing & transport allowance
- Stock (RSU's) and performance related bonus
- 16 weeks fully paid Maternity Leave
- 6 weeks fully paid Paternity Leave
- Employee stock purchase scheme
- Child Education Allowance
- Relocation and immigration support (if needed)
- Life and Medical Insurance
- Live+ Well Reimbursement for health and recreational membership fees
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
OR
PhD in Engineering, Information Systems, Computer Science, or related field.
- 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.
- References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
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If you would like more information about this role, please contact Qualcomm Careers.