Main Responsibilities

• Improve and optimise LLM inference performance across distributed, multi‑chip and multi‑node environments

• Apply strong understanding of transformer architectures, including dense and Mixture‑of‑Experts (MoE) models

• Benchmark leading LLMs (LLaMA, Mistral, Qwen, DeepSeek) across varied hardware stacks

• Design and implement attention‑level optimisations (Flash Attention, grouped‑query, sliding‑window)

• Deliver model‑level optimisation including quantisation (INT8/FP8), KV‑cache strategies, batching and parallelism

• Work closely with hardware, systems and compiler teams to co‑design efficient inference pipelines

• Build and maintain benchmarking frameworks to measure latency, throughput and scaling behaviour

• Evaluate architectural trade‑offs and contribute to deployment strategies for large‑scale environments

• • Stay current with research across LLM architectures, inference optimisation and performance engineering