About this role
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
Company:
Nicoll Curtin
Qualifications:
Language requirements:
Specific requirements:
Educational level:
Level of experience (years):
Senior (5+ years of experience)