Infrastructure & Site Reliability Engineer – Datacentre AI Engineering - Riyadh, KSA
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Key skills for this role
About the Role
Qualcomm is hiring a Site Reliability Engineer for Datacentre AI Engineering in Riyadh. You will design, deploy, and operate large-scale AI inference systems, ensuring reliability and scalability.
Key Skills for This Role
Responsibilities
- Design, deploy, and operate large scale AI inference systems supporting critical AI workloads
- Ensure reliability, availability, and scalability of Qualcomm datacenter AI clusters
- Develop and maintain software tools and support infrastructure around AI software stacks
- Analyze software requirements and collaborate with architecture and hardware engineers to support AI workloads
- Build, deploy, and operate components supporting LLM inference, agentic AI workflows, and AI services
- Apply SRE fundamentals including monitoring, alerting, incident response, and performance optimization
- Build and maintain observability tools, dashboards, and alerts to monitor system health and reliability
- Develop automation to reduce manual operational tasks and improve system reliability
- Support CI/CD pipelines for AI service and agent deployment
- Apply Infrastructure as Code practices using tools such as Terraform and Ansible
Requirements
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience (or Master's with 3+ years, or PhD with 2+ years)
- 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
- Strong programming skills in Python with experience building and supporting production systems
- Strong Linux fundamentals including shell, containers, system services, and networking basics (DNS, TLS, HTTP/gRPC)
- Experience working with AI/ML workloads such as LLMs, NLP, Vision, Audio, or Recommendation systems
- Hands on experience with monitoring and logging tools such as Prometheus, Grafana, ELK, or Loki
- Solid understanding of SDLC, release processes, and operational reliability practices
Full Job Posting
General Summary
- 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.
- We are looking for a Site Reliability Engineer or Senior Engineer – Datacentre AI Engineering at Qualcomm Technologies, Inc., located in Riyadh, Saudi Arabia.
- The role focuses on the design, operation, and continuous improvement of large‑scale AI inference systems in a datacenter environment.
Key Responsibilities
- Design, deploy, and operate large‑scale AI inference systems supporting critical AI workloads.
- Ensure reliability, availability, and scalability of Qualcomm datacenter AI clusters.
- Develop and maintain software tools and support infrastructure around AI software stacks.
- Analyze software requirements and collaborate with architecture and hardware engineers to support AI workloads.
- Build, deploy, and operate components supporting LLM inference, agentic AI workflows, and AI services.
- Work with models, systems, and software teams to improve model performance on AI100 deployments.
- Identify and implement optimizations for workloads running on multi‑SoC and multi‑card systems.
- Apply SRE fundamentals including monitoring, alerting, incident response, and performance optimization.
- Support production ML systems using MLOps tools and operational best practices.
- Contribute to incident reviews, operational documentation, and continuous reliability improvements.
- Build and maintain observability tools, dashboards, and alerts to monitor system health and reliability.
- Monitor infrastructure and services using tools such as Prometheus, Grafana, CloudWatch, and custom telemetry.
Required Skillset
- Experience working with AI/ML workloads such as LLMs, NLP, Vision, Audio, or Recommendation systems.
- Understand ML inference concepts including batching, token streaming, and performance considerations.
- Hands‑on experience with PyTorch and familiarity with modern ML frameworks.
- Familiarity with distributed inference, checkpointing, and accelerator‑based compute environments.
- Experience supporting AI or ML applications in production environments.
- Familiarity with LLM inference pipelines and AI service operations.
- Strong programming skills in Python with experience building and supporting production systems.
- Experience with scripting and automation using Python and Bash.
- Familiarity with configuration management and orchestration tools.
- Strong Linux fundamentals include shell, containers, system services, and networking basics (DNS, TLS, HTTP/gRPC).
- Experience working with cluster schedulers such as Slurm or equivalent systems.
- Experience operating distributed systems with high availability and fault tolerance.
Preferred Skillset
- Experience with GenAI, Agentic AI systems, or LLM orchestration frameworks.
- Exposure to LangChain, AutoGen, or RAG‑based systems.
- Experience with additional ML frameworks such as TensorFlow, JAX, or Ray.
- Knowledge of GPU/accelerator‑based systems and high‑performance networking (RDMA, InfiniBand, RoCE).
- Experience with advanced MLOps workflows or large‑scale AI platform operations.
Qualifications
- Bachelor’s or Master’s degree in engineering, Computer Science, AI/ML, or a related field.
- 2–8 years of software, systems, or infrastructure engineering experience, preferably in production or datacenter environments.
What's On Offer
- 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
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