Foundation Model Engineer: Build & Optimize LLM Pipelines

Pure Resourcing Solutions; Cambridge; On-site; GBP 90,000 - 150,000; Full time

Job summary

Pure Resourcing Solutions is recruiting a Foundation Model Engineer in Cambridge to advance end-to-end LLM development, training, evaluation and deployment. The role emphasizes building scalable ML pipelines, data handling and performance optimization.

You will work across experimentation through deployment, collaborating with researchers and engineers to push safe, responsible AI and deliver robust model systems from lab to production.

Qualifications

• Experience training and fine-tuning LLMs or multimodal models
• Understanding evaluation, bias/variance trade-offs, and data quality/feature engineering
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Experience building and maintaining ML pipelines in production
• Familiarity with GPU usage and optimisation
• Ability to debug and improve model performance systematically

Responsibilities

• Design end-to-end LLM training pipelines
• Source and preprocess datasets for training and evaluation
• Fine-tune and optimise open weight models (LLMs, vision or traditional ML)
• Build evaluation frameworks and define performance metrics
• Develop and maintain data pipelines and training workflows
• Analyse training pipelines and optimise for latency, cost and scalability
• Implement monitoring, logging and feedback loops for continuous improvement
• Experiment with modern AI tooling and services to assess usefulness

Skills

LLM training

Model evaluation

Python

ML pipelines

GPU optimisation

Debug performance

Tools

PyTorch

TensorFlow

Job description

Pure Resourcing Solutions is recruiting a Foundation Model Engineer in Cambridge to advance end-to-end LLM development, training, evaluation and deployment. The role emphasizes building scalable ML pipelines, data handling and performance optimization.

You will work across experimentation through deployment, collaborating with researchers and engineers to push safe, responsible AI and deliver robust model systems from lab to production.