Why this opportunity stands out
Build and ship production machine learning systems with real ownership, from the training code through to the pipelines and monitoring that keep them reliable in production.
About the role
We are looking for a Senior AI Engineer to design, build and run machine learning systems that solve real problems for the organisations we work with. You will take models from early experimentation all the way into production, working closely with data scientists, machine learning engineers and the client engineering teams who will ultimately run what you build.
This is a hands-on engineering role. You will write the training and inference code, shape the data pipelines that feed it, and make the systems you deploy reliable, observable and maintainable once they are live. You will be comfortable explaining your design decisions to technical stakeholders and pragmatic about where deep learning is genuinely the right answer and where it is not.
Key responsibilities
Write production-quality Python for training, inference and the services that wrap them.
Build and maintain the data pipelines that supply training and live features.
Partner with data scientists to turn experiments into reliable, deployed systems.
Instrument models in production, monitor performance and retrain when the data drifts.
Work with client engineering teams to integrate models into existing platforms and workflows.
Take part in design reviews, technical planning and the engineering hiring process.
Keep the security, privacy and explainability of AI systems in scope throughout delivery.
Document systems and decisions clearly enough for others to pick up and extend.
What they're looking for
Practical experience training, evaluating and deploying machine learning models in production.
Hands-on experience with PyTorch or TensorFlow, and with the Hugging Face ecosystem.
Solid understanding of data engineering: SQL, pipelines, versioning and data quality.
Experience with modern LLM and generative AI tooling, including retrieval-augmented generation and evaluation.
Familiarity with MLOps practices: experiment tracking, model registries, feature stores and deployment pipelines.
Strong grounding in data science fundamentals: statistics, experimentation and model evaluation.
Comfort working with cloud data and compute platforms, such as AWS, GCP or Azure.
Excellent communication, able to explain technical choices to non-specialist stakeholders.
Benefits
Hybrid working in London, with a flexible approach to office and remote time.
28 days of annual holiday, plus the usual public holidays.
Private medical cover and a company pension scheme with employer contributions.
A generous learning and development budget, plus dedicated time for training and certifications.
Exposure to a wide range of sectors and genuinely challenging applied problems.
A collaborative engineering culture with real ownership of what you ship.