AI Platform Engineer (Inference)

Requirements:

• 9+ years of backend development / distributed systems; Python or Go.
• Hands-on experience with one or more inference frameworks: vLLM, Triton, TensorRT-LLM, ONNX Runtime, PyTorch.
• Understanding of distributed training: data parallelism, tensor parallelism, pipeline parallelism, ZeRO.
• Knowledge of Kubernetes and cloud-native ecosystem; containerization and service orchestration.
• Proficiency in GPU cluster management fundamentals (CUDA, cuDNN, device plugins).
• Strong distributed systems design: load balancing, HA, resource scheduling.

Nice-to-Have:

• LLM serving at scale (vLLM, SGLang, TensorRT-LLM) in production.
• Quantisation techniques: INT8/FP8 sparsity, speculative decoding.
• Large-scale GPU training cluster operation (Slurm, Volcano, KubeRay).
• Training fault recovery, checkpoint management, elastic training.
• RDMA/NCCL/InfiniBand high-performance networking.
• Active open-source contributions (vLLM, SGLang, Megatron-LM, DeepSpeed, Ray).