Responsibilities
Ensure customer success with AI training clusters from setup to production.
Understand customer workloads, GPU requirements, networking, and storage needs.
Design cluster architectures using Kubernetes, Slurm, Ray, and related tools.
Support pilots, performance testing, and production deployment.
Troubleshoot distributed training, networking, storage, and GPU-related issues.
Create technical guides, reusable solutions, and troubleshooting documentation.
Share customer feedback with engineering teams to improve the platform.
Support pre-sales evaluations and post-sales technical needs.

Preferred candidate profile
Experience in ML engineering, platform engineering, solutions architecture, or production training workloads.




Understanding of distributed training and large-scale compute systems.
Practical knowledge of Kubernetes, Linux, containers, networking, and storage.
Robust debugging and problem-solving skills.
Excellent written and verbal communication skills.
Experience with Slurm, Ray, PyTorch, DeepSpeed, NCCL, or multi-node training is a plus.
Knowledge of GPU infrastructure, InfiniBand/RDMA, and HPC operations is an advantage.
Ability to write scripts for automation, benchmarking, and debugging.

📌 Ai Engineer At A Fintech Company Bengaluru
🏢 Talentzo Delhi
📍 Bengaluru