Understanding Recruitment
Machine Learning Engineer
Get notified of more jobs like this · No spam, ever
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
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
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
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
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London