Senior AI Engineer: LLM Inference & Systems Optimization

Client of Gateworth Group; Dubai; On-site; AED 420,000 - 640,000; Full time

Job summary

Gateworth Group is partnering with a UAE-based technology organization expanding its AI engineering team to push large-scale model performance. You will dive deep into transformer internals, optimize LLM inference across multi-chip and multi-node systems, and help shape deployment strategies for large-scale AI infrastructure.

The role focuses on benchmarking, profiling, and implementing optimisations for attention mechanisms, quantisation and KV caching, collaborating with hardware and compiler

Qualifications

• Hands-on experience with modern LLMs and transformer internals.
• Experience with multi-chip or multi-node inference.
• Strong Python and ML framework skills.
• Experience with quantisation, pruning, KV caching, batching, and parallelism.

Responsibilities

• Analyse profile and optimise LLM inference performance across distributed multi chip or multi node systems
• Apply deep understanding of transformer architectures including dense and MoE models
• Evaluate and benchmark leading LLMs (LLaMA, Mistral, Qwen, DeepSeek) across hardware environments
• Design and implement optimisations for attention mechanisms such as Flash Attention and sliding window attention
• Work on model level optimisation techniques including quantisation, pruning, KV caching, batching and parallelism strategies
• Collaborate with hardware systems and compiler teams to co design efficient inference pipelines
• Build and maintain benchmarking frameworks to evaluate latency, throughput and scaling behaviour
• Analyse trade offs between model architecture choices and system level performance for deployment strategies for large-scale environments
• Stay current with research in LLM architectures and inference optimisation

Skills

Transformer architectures

MoE architectures

Python programming

Distributed systems

Performance profiling

Tools

PyTorch

JAX

Job description

Gateworth Group is partnering with a UAE-based technology organization expanding its AI engineering team to push large-scale model performance. You will dive deep into transformer internals, optimize LLM inference across multi-chip and multi-node systems, and help shape deployment strategies for large-scale AI infrastructure.

The role focuses on benchmarking, profiling, and implementing optimisations for attention mechanisms, quantisation and KV caching, collaborating with hardware and compiler