AI / ML Engineer | AI Start-Up | London Hybrid | Up to £100,000

AI / ML Engineer | AI Start-Up | London Hybrid | Up to £100,000

We are partnered with an ambitious early-stage technology company building AI-powered products that are transforming highly specialised financial markets. Their platform combines cutting-edge machine learning with real-world trading workflows, giving engineers the opportunity to solve challenging technical problems while having genuine influence over product direction.

Working within a small, highly experienced engineering team, you'll collaborate directly with founders, engineers, brokers, and traders to build intelligent systems that have an immediate commercial impact. You'll be given significant autonomy, ownership from day one, and the opportunity to help shape the future of AI within a fast-growing business.

What you'll be doing

• Developing and deploying LLM-powered applications using PyTorch and SGLang.
• Building, fine-tuning, and optimising embedding and reranking models.
• Implementing parameter-efficient fine-tuning techniques, including PEFT and LoRA adapters.
• Designing data curation pipelines, including synthetic data generation for model training and evaluation.
• Working closely with product teams and domain experts to translate complex trading workflows into practical AI solutions.
• Running experiments, evaluating model performance, and rapidly deploying improvements into production.
• Contributing to the infrastructure supporting the training, evaluation, and deployment of machine learning systems.
• Python
• PyTorch (Torch)
• SGLang
• PEFT / LoRA
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Git
• Docker / Containers
• Microservices

What they're looking for

• Strong commercial experience building AI/ML applications using Python and PyTorch.
• Hands-on experience working with Large Language Models (LLMs).
• Experience building and optimising embedding and reranking models.
• Experience creating, cleaning, and curating datasets, including synthetic data generation.
• Strong understanding of machine learning fundamentals, including evaluation, optimisation, and model generalisation.
• Familiarity with Git, containers, microservices, and modern engineering practices.
• Comfortable working with numerical data and analytical problems.

Nice to have

• Experience deploying machine learning systems into production.
• Familiarity with Retrieval-Augmented Generation (RAG) architectures.
• Experience with prompt engineering or hybrid retrieval systems.
• Experience building low-latency or real-time AI systems.
• Cloud infrastructure experience.
• Previous experience working in a start-up or highly autonomous engineering environment.
• Exposure to financial markets or trading technology (not essential).