Responsibilities
THE TEAM:
AMD's Data Center GPU organization is transforming the industry with our AI based Graphic Processors. Our primary objective is to design exceptional products that drive the evolution of computing experiences, serving as the cornerstone for enterprise Data Centers, (AI) Artificial Intelligence, HPC and Embedded systems. If this resonates with you, come and joining our Data Center GPU organization where we are building amazing AI powered products with amazing people.
THE ROLE:
As a Cloud and Customer Solutions Engineer on AMD's Applied AI team, you will embed directly with AMD's most strategic AI customers — frontier labs, NeoCloud providers, CSPs, and AI-native companies — to take AMD Instinct GPU clusters from delivery to sustained production excellence.
You own the customer outcome end-to-end: cluster bring-up and certification, workload deployment and performance, production incident response, and the transfer of operational capability that moves customers toward autonomous operation of their AMD fleets.
To be direct about what this role is: despite the "Solutions" title, this is not a pre-sales or demo role. You will write production code, operate live clusters, carry accountability for customer production outcomes, and be the engineer in the room when things break at scale. What you learn in the field, you convert into durable improvements — to ROCm, to the open-source serving ecosystem, and to the reference architectures every subsequent deployment inherits.
THE PERSON:
You are a strong production engineer who is energized rather than drained by ambiguity, customer pressure, and environments you do not control. You can debug a distributed training hang at 2am, explain the root cause to a customer VP at 9am, and land the fix upstream by the end of the week. You measure success by customer production outcomes, not code merged or tickets closed. When something is broken on a cluster you touch, it is your problem until it is fixed or explicitly handed off.
KEY RESPONSIBILITIES:
- Own customer deployments end-to-end: cluster bring-up and burn-in, production readiness certification, workload onboarding, performance validation, and sustained production operation on AMD Instinct GPU fleets
- Deploy and tune large-scale training and inference stacks (ROCm, vLLM, SGLang, RCCL, Kubernetes, Slurm) against customer-specific workloads and SLOs across cloud, NeoCloud, and bare-metal environments
- Lead root-cause analysis and resolution of production incidents on customer clusters, including Sev-1 response, and drive fixes to permanent closure
- Deploy agentic AI solutions into customer environments in partnership with Agentic Data Engineers, and own their production behavior within the engagement
- Build the observability, benchmarking, and validation tooling needed to certify clusters as production-ready and keep them there
- Transfer operational capability to customer teams — documentation, runbooks, and hands-on enablement — moving customers up the operator-autonomy ladder from assisted operation to independent production ownership
- Contribute field learnings to the Applied AI team's skills library and engagement memory databases, so deployment knowledge compounds across the practice
- Convert field findings into upstream contributions — ROCm issues and patches, serving-framework improvements, reference-architecture updates — and provide structured field signal to AMD product, software, and silicon teams
- PREFERRED EXPERIENCE: 5+ years of production software or infrastructure engineering, including significant time operating or deploying systems in environments you did not build (level flexible for exceptional candidates)
- Hands-on experience with GPU compute at scale: cluster deployment, distributed training or high-throughput inference, performance debugging, and workload optimization
- Strong working knowledge of the modern AI infrastructure stack: Kubernetes and/or Slurm, containerized GPU workloads, collective communication libraries (RCCL/NCCL), high-performance networking (RoCE/InfiniBand), and observability tooling (Prometheus, Grafana)
- Cloud platform depth (AWS, Azure, GCP, or NeoCloud environments), including hybrid and bare-metal deployment patterns
- Proficiency in Python and at least one systems language; comfort navigating and modifying large codebases you did not write
- Working familiarity with LLM application patterns — inference serving, RAG, and agentic workflows — sufficient to deploy and troubleshoot them in customer environments
- Direct customer-facing experience: embedded deployments, technical escalations, on-site engagements, or equivalent
- Experience with ROCm and AMD Instinct GPUs strongly preferred; deep CUDA-ecosystem experience with demonstrated ability to work cross-platform also valued
- Open-source contribution history in AI/ML infrastructure projects is a plus
- PREFERRED ACADEMIC CREDENTIALS: Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience
- This role is not eligible for visa sponsorship.
Qualifications
