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!