Machine Learning Engineer

60,000 - 70,000

Hertfordshire Machine Learning Engineer If you’re an ML Engineer who enjoys shipping and running AI systems in production more than spending your days experimenting with models, this could be a very good fit.

What’s in it for you?

Work on AI and LLM systems that are genuinely running in production

Own problems across development, infrastructure and deployment, rather than being boxed into one area Build with modern GenAI technologies including RAG, agentic AI and LLMsSignificant exposure to AWS architecture, MLOps, CI/CD and observability

Freedom to improve how AI services are deployed, monitored and scaled

Opportunity to take increasing technical ownership and potentially step into a Senior/Lead role Remote working with the option to spend time in the office

What you’ll be working on Building and operating production AI/LLM services

Designing and scaling cloud infrastructure in AWS Improving CI/CD, infrastructure-as-code and automated deployments

Developing and debugging Python services using tools such as FastAPI and Pydantic Building production RAG pipelines, including embeddings, indexing, retrieval and reranking

Implementing monitoring, tracing and observability across AI services

Improving system reliability, performance, compute efficiency and cost Owning technical problems from development and staging through to production

What we’re looking forYou’ll ideally have 4+ years of relevant engineering experience, although depth of experience matters more than an exact number.

The strongest fit will be someone with:

A background in ML Engineering, MLOps, Platform Engineering or Software Engineering Strong Python development experience

Hands-on experience building and operating systems in AWS Experience deploying and maintaining production ML or AI services

Good understanding of CI/CD, containers and infrastructure-as-code

Experience with monitoring and observability tools such as Grafana, Cloud Watch, Langfuse or similar

Some practical exposure to LLMs, RAG, NLP or generative AIThe confidence to take ownership of production systems and help guide other engineers