AI Engineer at A FinTech Company

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.
• Strong 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.