System level AI Solutions & Software Engineer; level KSA

Company

Qualcomm Middle East Information Technology Company LLC

Engineering Group, Engineering Group >
Systems Engineering

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 s 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.

As a Qualcomm Datacenter AI Solutions Engineer, you will research, develop, optimize, and validate software, hardware, architecture, algorithms, and machine learning solutions that enable the deployment of cutting‑edge AI datacenter technology.

Qualcomm Solution Engineers collaborate across functional teams to meet and exceed system‑level requirements and standards. This is a great opportunity to innovate and develop leading‑edge products and solutions around best‑in‑class Qualcomm AI inference accelerators for data center, and hybrid AI applications.

• Lead the development of end‑to‑end AI/ML solutions that integrate Qualcomm AI hardware, system software, and ecosystem components to deliver best‑in‑class AI inference performance, power efficiency, and scalability
• Drive the design, development, deployment, and optimization of Generative AI and LLM‑based applications, with a focus on production readiness and inference efficiency
• Contribute to and guide the implementation of model fine‑tuning, distillation, and optimization strategies tailored for deployment on target hardware
• Apply deep systems‑level expertise to research, design, develop, simulate, validate, and optimize AI systems spanning hardware, system software, AI frameworks, and models, while ensuring system‑level requirements are met
• Perform AI model benchmarking, workload characterization, and performance analysis to influence system requirements, hardware/software co‑design, and product direction
• Serve as a technical lead for customer engagements, supporting AI model onboarding, inference optimization, deployment, and performance tuning
• Own and drive system‑level architecture and design, including requirements definition, interface specifications, performance targets, and implementation of new systems or enhancements to existing platforms
• Collaborate across cross‑functional teams (hardware, software, tools, frameworks, and product) to deliver features, validate AI system correctness, and ensure high‑quality execution
• Stay current with advancements in AI/ML models, inference techniques, and hardware/software innovations, and proactively translate them into impactful solutions
• Propose and drive new, innovative ideas that meaningfully improve products, platforms, or developer experience
• Lead system‑level debugging and triage, identify root causes across the stack, and clearly communicate findings, trade‑offs, and recommendations to team members and stakeholders

• Master’s or PhD in Engineering, Computer Science, Information Systems, Physics, or a related discipline
• Strong proficiency in Python and experience with ML frameworks, APIs, REST services, and microservice‑based architecture
• Hands‑on experience designing, deploying, and operating AI/ML systems in production
• Solid understanding of Generative AI architectures, including transformers, diffusion models, and hybrid systems (LLMs, LVMs, embeddings)
• Experience with large‑scale AI systems architecture, including microservices, distributed systems, event‑driven designs, and fault‑tolerant/resilient architectures
• Practical experience with AI inference serving, performance optimization, and scalability across heterogeneous hardware
  
  Experience with MLOps practices for AI application development, deployment, monitoring, and lifecycle management
• Familiarity with automation and Dev Ops tooling, including Git Ops workflows, containerization (Docker), orchestration platforms…