ML Operations & Customer Support Engineer, Staff/Senior Staff level - Riyadh, KSA

About Us

About The Role

Machine Learning Operations & Customer Support Engineer

Customer Support & SLA Ownership

• Act as the primary technical escalation point for customer issues related to AI inference workloads
• Own end-to-end case management, ensuring resolution within agreed SLAs and KPIs
• Drive incident response, triage, and root cause analysis (RCA).
• Provide timely and transparent communication to customers on issue status and resolution
• Maintain high levels of customer satisfaction and service reliability

Uptime, Reliability & Operations

• Ensure high availability and uptime of customer AI deployments (rack-scale systems)
• Monitor system health, performance metrics, and workload behavior
• Implement and manage failover, redundancy, and resiliency mechanisms
• Proactively identify risks and implement preventative actions

AI Inference Workload Support

• Support deployment, optimization, and troubleshooting of ML inference pipelines
• Debug issues across model, runtime, system, and hardware layers
• Analyze model performance (latency, throughput, accuracy tradeoffs) in production
• Support frameworks such as PyTorch, TensorFlow, ONNX, and model conversion flows
• Assist in model optimization techniques (quantization, batching, compilation, runtime tuning)

System & Infrastructure Engineering

• Support bare-metal and virtualized environments for AI workloads
• Troubleshoot issues across Linux OS, drivers, firmware, and networking stack
• Support deployment and maintenance using Infrastructure as Code (IaC) and automation tools
• Work with DCIM tools and monitoring systems for infrastructure visibility
• Coordinate with hardware vendors for accelerator, server, and networking issues

Monitoring, Observability & Automation

• Implement and manage monitoring systems (logs, metrics, traces)
• Build dashboards for uptime, SLA adherence, performance, and utilization
• Automate repetitive operational tasks using scripts and workflows
• Establish and enforce runbooks and standard operating procedures (SOPs)

Cross-Functional Collaboration

• Work closely with Customer Engineering, Product, Engineering, and Support teams
• Provide structured feedback to engineering for product improvements and defect resolution
• Support customer onboarding, deployment readiness, and operational handover
• Participate in customer reviews, escalations, and technical deep dives

Required Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or related field
• 10–15+ years of experience in ML operations, systems engineering, or customer support engineering
• Proven experience in customer-facing technical roles with SLA-driven support models
• Strong experience with AI/ML inference workloads in production environments
• Deep understanding of end-to-end ML inference pipelines
• Hands-on experience with Linux systems, system bring-up, drivers, and debugging tools
• Strong understanding of AI accelerator architecture and system bottlenecks
• Experience with model deployment, optimization, and performance tuning
• Experience with data center operations and rack-scale deployments
• Familiarity with bare-metal, virtualization, and containerization technologies (Docker, Kubernetes)
• Knowledge of networking concepts (TCP/IP, RDMA, storage systems)
• Experience with cloud and hybrid environments
• Experience with monitoring/observability tools (Prometheus, Grafana, ELK, etc.)
• Strong skills in incident management, RCA, and production operations
• Experience defining and tracking SLAs, KPIs, and operational metrics
• Proficiency in Python, Bash, or similar scripting languages
• Experience in automation, DevOps, and MLOps tooling
• Strong problem-solving and diagnostic skills
• Excellent communication and customer engagement skills
• Ability to operate in high-pressure, mission-critical environments
• High attention to detail with a focus on quality, reliability, and accountability

Preferred Qualifications

• Experience with Qualcomm Cloud AI or similar AI accelerator platforms
• Experience supporting large-scale AI deployments (LLMs, CV pipelines, generative AI)
• Familiarity with inference runtimes (TensorRT, ONNX Runtime, custom runtimes)
• Experience with CI/CD pipelines for ML deployment

Why Join Qualcomm

What's On Offer

• 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

Minimum Qualifications

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.

OR

OR

• PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
• 2+ years of 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 is an equal opportunity employer.
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• You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here.
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