Machine Learning Engineer

Role: Machine Learning Engineer (PE Fund)

Location: Central London, Hybrid

Salary: Above market rate, circa £150k-£200k

This is a chance to work with one of the world’s largest PE Funds as build a brand-new AI & Data Science capability internally, which will drive how the £150bn+ fund operates. Greenfield opportunities are rare, coupled with the chance to work with some of the brightest minds. Your peers have helped scale some of the fastest growing brands globally.

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.

We are looking for more ML Ops and LLM application development experience, rather than purely optimization of algorithms or novel algorithm design.

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 (DBP, Snowflake)
• Strong MLOps experience, pipelines and orchestration tools (e.g., Airflow, Prefect, or Azure Data Factory).
• PE, Investments, Fintech, or similar sector experience would be highly advantageous, but not essential.
• CI/CD Automation - Expertise in GitHub Actions to streamline the deployment of ML models and AI applications, would be highly advantageous.

The office is in Central London (hybrid), with a split between office and home working. Well-suited to a hands on Principal ML Engineer