Senior AI Engineer
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Key skills for this role
Role Overview
Gateworth Group seeks a Senior AI Engineer to optimize LLM inference performance, work on transformer architectures, and contribute to next-generation AI systems.
The role requires strong Python skills, experience with modern LLMs, and a background in distributed systems and system-level optimization.
Key Skills for This Role
Full Job Posting
Overview
Gateworth Group seeks a Senior AI Engineer to optimize LLM inference performance, work on transformer architectures, and contribute to next-generation AI systems.
The role requires strong Python skills, experience with modern LLMs, and a background in distributed systems and system-level optimization.
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
Qualifications
- Strong understanding of transformer architectures, LLM internals and both dense/MoE models
- Hands-on experience with modern LLMs (LLaMA, Mistral, Qwen, DeepSeek) and attention-level optimisation
- Practical experience with inference optimisation: quantisation (INT8/FP8), KV-cache strategies, batching, pruning and parallelism
- Strong Python skills with PyTorch or JAX, plus experience profiling and debugging performance bottlenecks
- Background in distributed systems, large-scale inference workloads and system-level optimisation across hardware and runtime layers
- Ideally 8+ years in deep learning, AI systems or performance engineering, with exposure to datacenter-scale inference (e.g., vLLM) and hardware-aware optimisation
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