Platform Engineer (AI Infrastructure)

LLMOps Engineer (AI Platform & Operations)
£80,000 – £95,000 | Fully Remote (UK)
*Sponsorship Not Available*
My client is a well-established, private equity-backed technology-enabled business making significant investment in AI and the development of intelligent products and services.
As part of this investment, they're looking for an LLMOps Engineer to join a growing AI team and take ownership of the performance, reliability and ongoing improvement of AI applications running in production.
This is an opportunity to work with modern LLMs and agentic AI systems, ensuring they're not only deployed successfully but continue to perform accurately, reliably and efficiently as models, data and business requirements evolve.
It's a hands-on engineering position sitting somewhere between MLOps, Software Engineering and AI Platform Operations.
The Role
You'll work closely with AI Engineers, Data Scientists and wider engineering teams, taking responsibility for the day-to-day operation and performance of production AI systems.
Key responsibilities include:
Monitoring, troubleshooting and improving the reliability of production LLM applications and AI platforms.
Investigating issues across AI workflows, data pipelines, integrations and underlying cloud infrastructure.
Supporting the operation of agentic AI systems, including identifying and resolving issues with orchestration, tool calls and multi-step workflows.
Building and maintaining monitoring, observability and evaluation processes to ensure AI outputs remain accurate and consistent.
Managing model upgrades, deployments and configuration changes, including testing, rollback and fallback strategies.
Identifying performance degradation, unexpected model behaviour and opportunities to improve efficiency.
Working with engineering teams to improve operational processes, documentation and overall platform reliability.
What We're Looking For
We're interested in speaking with engineers who have experience running production software or AI systems and a genuine understanding of how modern LLM applications work.
Ideally, you'll bring:
Experience in LLMOps, MLOps, AI Engineering, Platform Engineering or a closely related discipline.
Hands-on experience supporting and troubleshooting production applications.
A good understanding of LLMs, including prompting, context management, model behaviour and output quality.
Experience with Python for automation, debugging or application development.
Experience working with AWS and cloud-hosted applications.
Familiarity with monitoring, logging, observability and CI/CD processes.
An understanding of agentic AI systems, LLM evaluation or model monitoring.
Strong problem-solving skills and the ability to investigate issues across complex systems.
Nice to Have
Experience with RAG pipelines, vector databases or retrieval systems.
Exposure to LangChain, LangGraph or other agent orchestration frameworks.
Familiarity with MLflow, Databricks, Langfuse, Helicone or similar tooling.
Experience managing multiple LLM providers, model routing or fallback strategies.
Previous experience building or operating production GenAI applications.
Why Consider It?
This is an opportunity to join a business investing heavily in AI, with the financial backing, established customer base and long-term ambitions to develop and scale AI-powered products.
Unlike many early-stage AI startups, the business offers the opportunity to work with emerging technologies within a more established and commercially secure environment.
You'll have genuine ownership of production AI systems, work closely with an experienced AI leadership team and help shape how LLMOps is delivered across the organisation.
If you're an engineer who enjoys the challenge of making AI systems work reliably in the real world, I'd be keen to hear from you.

Work arrangement
Yes