Data Scientist
Senior AI Engineer (Data Science) – LLM Products
Our client is looking for a hands‑on Senior AI Engineer who loves working at the intersection of data science, product, and cutting‑edge LLMs. You’ll help design, build and refine AI systems that power real‑world products for large, complex organisations – not just proof‑of‑concepts. If you enjoy taking new research, stress‑testing it on real data, and turning it into something that actually ships, you’ll feel at home here.
What you’ll do
- Designing, training and deploying machine learning models that directly shape our product experience and outcomes.
- Exploring large, messy datasets (structured and unstructured) to uncover patterns, edge cases and new opportunities.
- Tuning and evaluating LLMs across a range of use cases, with a strong focus on robustness and reliability.
- Owning parts of the MLOps lifecycle: experimentation frameworks, benchmarking, monitoring, QA and compliance.
- Helping to shape our approach to data: quality, governance, documentation and responsible AI.
- Working side‑by‑side with engineers, product managers and domain experts to ship useful features, not just models.
- Coaching and supporting less experienced data scientists and engineers, and contributing to a culture of high standards and open feedback.
What you’ll bring
You’re a practical AI expert: comfortable with theory, but happiest when it translates into impact. You can talk to engineers, product and business stakeholders without losing anyone along the way.
You’ll likely bring:
- 8 - 12 years’ experience as a Data Scientist or AI/ML Engineer working on production problems.
- Tertiary qualifications in Data Science, Maths, Statistics, Computer Science or similar (post‑grad a plus, not a deal‑breaker).
- Solid experience working with both structured and unstructured data.
- Strong SQL and at least 2+ years of day‑to‑day Python in a commercial setting.
- Experience applying statistical methods and clearly communicating findings to non‑technical audiences.
- Hands‑on NLP experience – e.g. LSTM/CNN/Transformers, supervised NLP, topic modelling.
- Practical experience with GPT‑style models and frameworks such as LangChain, LlamaIndex, RAG pipelines or multi‑agent setups.
- The ability to read and interpret recent LLM research and decide what’s worth testing in production.
- A collaborative mindset: you listen well, give and receive feedback, and care about the quality of the overall product, not just “your” model.
- Comfort working in a fast‑moving environment where priorities can shift as we learn.
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