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Role: Machine Learning Engineer (World Leader)
Location: London, Hybrid
Salary: Above market rate, circa £150k-£200k
This is a chance to work with a real-world leader that aren't just changing the game, they are creating it. Greenfield opportunities are rare, coupled with the chance to work with some of the brightest minds globally. Your peers have helped scale some of the fastest growing brands globally.
Operating within a Financial / Investment style organisation, but experience here isn't essential. The business is agile and nimble and won't suit large FS backgrounds.
This role will be a critical founding member of the businesses new data science and AI team. You will play a crucial role in defining ML and AI application development practices, implementing tooling, processes, and frameworks to efficiently build and deploy AI applications in production
This role offers high autonomy and a chance to pioneer innovative AI applications-from integrating LLM-based agents and chat interfaces to building end-to-end MLOps pipelines that streamline and elevate how we work.
Key Responsibilities
- Build end-to-end machine learning pipelines (including traditional ML and LLM-based approaches)
- Implement reusable custom cognitive architectures, agentic workflows, advanced RAG systems to deliver AI applications that set industry standards in controllability and answer quality.
- Design and implement CI/CD for ML models, ensuring seamless deployment and integration within a Snowflake-centric Azure environment.
Skills / Experience:
- Demonstrable experience building and deploying machine learning solutions in a production environment.
- Extensive experience in Python and Java/Scala with experience in ML frameworks (e.g., TensorFlow, PyTorch) and LLM application frameworks (e.g., langchain, autogen, llamaindex)
- Hands-on experience working in cloud-based data ecosystems
- Familiarity with MLOps pipelines and orchestration tools (e.g., Airflow, Prefect, or Azure Data Factory).
This role is hybrid London, with a split between office and home working. Well suited to a hands on Principal ML Engineer
