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 LLMs
• Significant 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 for

You’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, CloudWatch, Langfuse or similar
• Some practical exposure to LLMs, RAG, NLP or generative AI
• The confidence to take ownership of production systems and help guide other engineers