ML Operations & Customer Support Engineer​/Senior level KSA

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 intelligent edge devices, next-generation computing platforms, and advanced AI solutions. Qualcomm’s leadership in AI, high‑performance compute, and connectivity is driving innovation across cloud, edge, and data center environments – delivering scalable, power‑efficient platforms that power the next generation of intelligent infrastructure.

Qualcomm is seeking a Machine Learning Operations & Customer Support Engineer within the Customer Engineering team to support strategic customers deploying AI inference workloads on advanced Qualcomm AI inference accelerators. These accelerators utilize Qualcomm's expertise in hardware-accelerated AI to deliver high-performance, energy-efficient generative AI and computer vision inference solutions for modern data centers. This is a customer‑facing, production‑critical role focused on ensuring maximum system uptime, reliability, and performance, while resolving customer support cases within defined SLAs/KPIs.

The role requires deep expertise across ML inference pipelines, systems troubleshooting, and data center operations, working closely with customers, internal engineering, and product teams.

• 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

• 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

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

• 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

• 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)

• 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

• 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…