You will act as a trusted advisor across LLM training, fine-tuning, RAG workloads, distributed training frameworks, storage throughput requirements, multi-GPU scaling, and performance tuning. This role requires deep technical fluency and exceptional customer management skills to help AI/ML teams achieve predictable, cost-efficient, high-performance outcomes.
Key Responsibilities
AI/GPU Onboarding & Workload Architecture
Lead onboarding for customers deploying GPU clusters (bare metal, VMs, or hybrid).
Advise on cluster design: multi-GPU topology, NVLink/NVSwitch considerations, RDMA, Infiniband and RoCE Ethernet, networking throughput, and storage IOPS requirements.
Guide customers in selecting GPU types and configurations based on workload (training, fine-tuning, inference, embeddings, RAG pipelines).
Support distributed frameworks: PyTorch, TensorFlow, DeepSpeed, Megatron, JAX, Ray, Mosaic, HuggingFace, etc.
Advanced hands on Kubernetes skills
Advanced hands on SLURM skills
Performance Optimization & Scaling
Identify bottlenecks (network, storage, memory bandwidth).
Provide tuning recommendations for batch size, mixed precision, parallelization strategies, and checkpointing.
Help customers evaluate cost vs. performance tradeoffs (GPU mix, CPU pairing, instance types, cluster sizing).
Technical Relationship Ownership
Own the long-term technical strategy across assigned GPU/AI accounts, including hyperscalers, labs, and high-growth AI startups.
Host recurring technical review meetings, roadmap reviews, and optimization sessions.
Define scaling plans, future GPU reservation needs, and capacity forecasting.
Incident & Escalation Management
Partner with Support, SRE, Networking, NOC, and Product Management & Engineering to resolve high-urgency incidents.
Manage outage communications, corrective action plans, and postmortem reviews with customers.
Advocate for GPU reliability improvements and influence roadmap priorities.
Account Growth & Expansion
Identify opportunities for expanded clusters, high speed storage, or networking upgrades.
Support Sales with technical validation and architecture diagrams needed for expansion.
Customer Advocacy & Product Feedback
Provide structured feedback on existing and future GPU offerings, networking fabrics, storage platforms, and upcoming AI/ML platform features.
Partner with Product on early access programs (new GPUs, pipelines, orchestration, etc.).
Qualifications
2–5+ years as an AI/ML Engineer, AI/ML Ops, Technical Account Manager, HPC Engineer, Sales/Solutions Engineer or relevant technical role.
Strong knowledge of GPU hardware architectures (NVIDIA/AMD), CUDA/ROCm, distributed training, and ML frameworks.
Experience with Linux tuning, networking (Infiniband, RoCE fabrics).
Experience with high-performance storage systems (DDN, NetApp, Vast, Weka, etc.).
Ability to communicate complex concepts clearly to both executives and engineering teams.
Prior experience supporting hyperscale, AI labs, or large cluster deployments is a plus.
Cloud Native Computing Foundation Certified Kubernetes Administrator (CKA) certification is a plus.
Compensation
$115,000 – $140,000
This salary can vary based on location, years of experience, background and skill set.
Inclusion & Privacy
We are an equal opportunity employer and are committed to creating an inclusive environment for all employees. We welcome applications from individuals of all backgrounds and experiences, and we prohibit discrimination based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status under applicable laws. Vultr will consider qualified applicants with arrest or conviction records in accordance with applicable laws and will not conduct a background check until after an offer of employment has been extended and accepted.
We also take your privacy seriously. We handle personal information responsibly and follow applicable laws, including U.S. privacy rules and India’s Digital Personal Data Protection Act, 2023. Your data is used only for legitimate business purposes and is protected with proper security measures.
Where allowed by law, applicants may request details about the data we collect, access or delete their information, withdraw consent for its use, and opt out of nonessential communications. For more details, please see our Privacy Policy.
