Asset Management AI Systems Engineer – AssociateVP

Key Responsibilities

• Inference Platform & Optimization:Build and optimize enterprise LLM serving platforms (e.g. vLLM TensorRT-LLM) using techniques like PagedAttention continuous batching and quantization (AWQ/FP8) for high throughput and low latency.
• GPU Pooling & AI Infra:Design GPU pooling virtualization and scheduling solutions on Kubernetes to maximize hardware utilization. Manage distributed training clusters and high-performance networking (RDMA/NCCL).
• Model Deployment & MLOps:Streamline the CI/CD pipeline for AI models. Implement automated benchmarking zero-downtime deployment and comprehensive observability (TTFT TPS GPU metrics).

Qualifications

1. Education & Experience:

• Bachelors Masters or Ph.D. in Computer Science Computer Engineering or a related field.
• 3 years of experience inBackend Systems Distributed Systems or AI Infrastructure/MLOps with at least 1-2 years specifically focused on LLM serving GPU optimization or ML Systems.

2. Core Engineering & Systems Skills:

• Expert-level proficiency inPythonand strong proficiency inJava(essential for inference engines and CUDA integration).
• Deep understanding ofLinux internals networking and distributed systemsarchitecture.
• Hands-on experience with container orchestration (Kubernetes Docker) and building custom K8s operators or controllers.

3. AI Infrastructure & Optimization Skills:

• Deep familiarity with LLM inference engines (vLLM TensorRT-LLM TGI) and understanding of their underlying architectural designs. Or
• Solid understanding of GPU architecture (NVIDIA Ampere/Hopper)CUDA programming and GPU memory management. Or
• Experience with distributed training frameworks (DeepSpeed Megatron-LM Ray) and high-performance networking (RDMA RoCE InfiniBand).

4. Mindset & Soft Skills:

• A hacker mindset with a passion for squeezing every drop of performance out of hardware.
• Ability to collaborate effectively with AI Researchers (to understand their models) and Backend Engineers (to integrate AI into business systems).

Preferred

• Contributions to open-source AI Infra projects (e.g. vLLM Ray PyTorch).
• Experience writing custom CUDA kernels or using Triton for operator fusion.
• Financial industry (Asset Management/Quant) experience is a plus.
• Language:Professional working proficiency in English to collaborate with global teams.