AI Engineer

Location: London (Hybrid – 1-2 days/week in office)
Reports to: Head of AI 

Key Responsibilities 

• Train and fine-tune large-scale foundation models for financial applications 

• Scale training across multi-GPU/TPU clusters with efficient parallelization 

• Manage compute resources and optimise training costs across cloud providers 

• Build efficient inference pipelines for production deployment 

• Implement distributed training strategies (data/model/pipeline parallelism) 

• Develop custom JAX/PyTorch implementations for novel architectures 

• Monitor and debug large-scale training runs with experiment tracking 

Essential Requirements 

• Experience training models with 1B+ parameters 

• Strong expertise in JAX and/or PyTorch at scale 

• Hands-on experience with multi-GPU/TPU training and optimization 

• Deep understanding of diffusion models and autoregressive architectures 

• Experience with distributed training frameworks (DeepSpeed, FSDP, Accelerate) 

• Experience with foundation model fine-tuning (LoRA, QLoRA, PEFT) 

• Strong understanding of attention mechanisms and transformer architectures 

Nice to Have 

• Experience with mixture-of-experts (MoE) architectures 

• Knowledge of quantization and model compression techniques 

• Experience with streaming/online learning at scale 

• Background in financial modeling or time series 

• Familiarity with MLOps tools (Weights & Biases, MLflow) 

What We Offer 

• Work on cutting-edge AI for major financial institutions (LSEG, Barclays) 

• Access to significant compute resources (GPUs/TPUs) 

• Direct impact on core AI technology 

• Equity package 

• Competitive salary 

• Flexible hybrid working 

 Email us to apply.

Looking forward to hearing from you!