VirtueTech Recruitment Group
Applied AI/ML Engineer | FinTech Start-Up in Trading | London Hybrid 4 days/week | Up to £100k + Bonus
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Applied AI/ML Engineer | FinTech Start-Up in Trading | London Hybrid 4 days/week | Up to £100k + Bonus
I’m working with an exciting, early-stage FinTech that is building AI systems for complex, data-intensive industries like commodity trading.
They’re looking for an Applied AI/ML Engineer who wants to work close to the founders and engineering team on a small team with significant ownership over how problems are solved. This is not an AI wrapper or prompt engineering role. They’re looking for someone who has genuinely worked with ML models which includes training, fine-tuning, evaluating, and improving them and enjoys figuring out difficult problems in new domains.
What you’ll be working on
- Developing and deploying LLM-powered systems using PyTorch
- Fine-tuning open-source models using LoRA / QLoRA / PEFT
- Building and improving embedding and reranking models
- Designing datasets and generating synthetic training data
- Working on semantic search and document intelligence systems
- Running experiments and building robust model evaluation frameworks
- Optimizing models for real-world production use cases
- Working closely with domain experts to turn complex workflows into ML solutions
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What we're looking for
You’ll ideally have hands-on experience with several of the following:
- PyTorch
- Transformer-based models such as BERT, RoBERTa, Llama, etc.
- LLM fine-tuning
- LoRA / QLoRA / PEFT
- Embedding models
- Reranking / cross-encoder models
- Semantic search / information retrieval
- Dataset construction, cleaning, or synthetic data generation
- ML experimentation and evaluation
- Hugging Face / Transformers or similar tooling
- Production ML, containers, and cloud infrastructure
You don't need to be a researcher who can train a transformer from scratch. They're much more interested in someone who can take existing models, understand how they work, adapt them to a new problem, and get them into production. Please note that academic, research, or R&D experience is absolutely relevant.
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Experience with RAG is useful, but this isn't a role for someone whose experience is primarily connecting APIs together with LangChain or similar frameworks.
I am particularly interested in people who understand what is happening underneath the RAG stack, how embeddings are trained and evaluated, how retrieval works, how rerankers work, how training data is created, and how models can be improved through experimentation. If you've actually trained or fine-tuned models, rather than simply used them, I'd be very interested in speaking with you.
The role is 4 days a week hybrid in London and is paying up to the £100k + bonus mark on the top of the band.
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