> Markdown version of [/jobs/ext/2809096-machine-learning-engineer-ai-llm-systems](https://www.wearedevelopers.com/jobs/ext/2809096-machine-learning-engineer-ai-llm-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer (AI / LLM Systems) - **Company:** Nicoll Curtin - **Location:** London, UK (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Microsoft Azure, Software Debugging, Python (Programming Language), Machine Learning, Tensorflow, Data Logging, Pytorch, Large Language Models, Backend, Low Latency, Machine Learning Operations, Docker - **Published:** September 9, 2026 - **Apply:** https://nlppeople.com/apply/8kqs ## About the Role 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, Senior (5+ years of experience) Tagged as: Industry, Machine Learning, NLP, United Kingdom ## Description 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 ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) - [LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices](https://www.wearedevelopers.com/videos/1582-llmops-driven-fine-tuning-evaluation-and-inference-with-nvidia-nim-nemo-microservices) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)