Senior Machine Learning Engineer; AI​/LLM Systems

Position: Senior Machine Learning Engineer (AI / LLM Systems)
Reference: 50926 Senior Machine Learning Engineer (AI / LLM Systems) Remote (United Kingdom) | Permanent | Flexible compensation + equity
We’re working with a well-established, tech-led business that is building a new AI product focused on real-world tasks, workflows, and decision-making.
This is a small, high-calibre team building systems where model capability is transformed into reliable, production-grade ML systems, with a strong emphasis on ownership, iteration, and real-world performance.
The product focuses on:
Long-running AI workflows Persistent context across interactions Multi-step reasoning and task execution Integration with external tools and systems The core challenge is designing ML systems that can behave reliably in production, even when model outputs are inherently non-deterministic.

The Role This role sits at the core of the ML layer powering the product.
The focus is on designing and operating systems that enable: ML models to run reliably in production End-to-end pipelines from training to inference Continuous evaluation and iterative improvement Systems that perform consistently under real usage conditions You’ll be working on:
Training, inference, and evaluation pipelines LLM-based systems and agent-style workflows Debugging model behaviour using real-world signals Optimising performance across latency, cost, and reliability Production monitoring, logging, and system stability What They Care About The hiring bar is centred around real production ML experience:
Whether you have:
Shipped ML systems used by real users Owned ML systems end-to-end in production Worked with modern LLMs beyond simple API integration Your exposure to practical challenges such as:
Model behaviour debugging and failure analysis Latency, throughput, and cost optimisation Monitoring, observability, and evaluation frameworks Scaling ML systems in production environments Tech Environment Python (core ML and backend language) PyTorch / modern ML frameworks LLM ecosystem (OpenAI, Anthropic, etc.) GPU-based training and inference Docker and Kubernetes AWS, Azure or GCP The emphasis is on how you build and operate ML systems, rather than specific tools.

Team & Working Style Fully remote-first, work from anywhere Small, highly capable engineering team Strong emphasis on ownership and delivery Fast iteration cycles with real user feedback Comfortable working in evolving systems and making pragmatic decisions