AI Infrastructure Engineer resume for GPU + inference roles
AI Infra roles want GPU depth, distributed training, and inference serving at scale. We rewrite yours around clusters you built and cost you optimized.
What every resume must prove
- GPU cluster management on Kubernetes
- Inference serving (vLLM, TGI, Triton)
- Distributed training (Ray, MPI, Horovod)
- Storage and networking for AI workloads
- Cost and utilization optimization
What makes you stand out
- NVIDIA GPU operator and MIG partitioning
- NCCL and RDMA tuning for multi-node training
- Spot/preemptible GPU strategy
- Multi-tenant GPU scheduling
- FinOps for GPU spend
Portfolio projects to put on your resume
GPU cluster on GKE
GKE · NVIDIA Operator · vLLM
Served 50M tokens/day at 40% lower cost than managed endpoints
Distributed training platform
Ray · NCCL · S3
Trained 70B model across 32 GPUs with 90% scaling efficiency
Inference cost optimization
vLLM · Kubecost · Prometheus
Cut GPU spend 35% via batching + MIG partitioning
ATS keywords for this role
Include these verbatim where they match your real experience. ATS systems scan for exact terms — but never lie. We help you place them truthfully during the rewrite.
Bullet templates you can copy
Built GPU cluster on {platform} serving {N}M tokens/day at {cost} costTrained {model} across {N} GPUs with {X}% scaling efficiencyCut inference cost {X}% via vLLM batching + MIG partitioningOwned AI infra for {N} teams, supporting {N} GPUs in productionReplace placeholders like {X}, {N}, {latency} with your real numbers.
Common mistakes to avoid
- No GPU or distributed training evidence
- Missing inference serving depth
- No cost or utilization numbers
- Describing app deployment instead of AI infra
- No multi-node or NCCL story
Get your AI Infra Engineer resume rewritten
Full Resume Rewrite at ₹4,999 (India) or $149 USD (international) — 5 days turnaround, 2 revisions, role-specific ATS optimization. 🎓 Students save 30%.