Summary of the role:
- Generative AI
- ML/Data Science
- Full time, permanent
- 3 days onsite, London
We are looking for a highly capable and innovative Data Scientist with experience in Generative AI to join our Data Science Team. You will lead the development and deployment of GenAI solutions, including LLM-based applications, prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG) for enterprise use cases.
As part of your duties, you will be responsible for:
• Design and build Generative AI solutions using Large Language Models (LLMs) for business problems across domains like customer service, document automation, summarization, and knowledge retrieval.
• Fine-tune or adapt foundation models using domain-specific data.
• Implement RAG pipelines, embedding models, vector databases (e.g., FAISS, Pinecone, ChromaDB).
• Collaborate with data engineers, MLOps, and product teams to build end-to-end AI applications and APIs.
• Develop custom prompts and prompt chains using tools like LangChain, LlamaIndex, PromptFlow, or custom frameworks.
• Evaluate model performance, mitigate bias, and optimize accuracy, latency, and cost.
• Stay up to date with the latest trends in LLMs, transformers, and GenAI architecture.
Qualifications and experience we consider to be essential for the role:
• 5+ years of experience in Data Science / ML, with 1+ year hands-on in LLMs / GenAI projects.
• Strong Python programming skills, especially in libraries such as Transformers, LangChain, scikit-learn, PyTorch, or TensorFlow.
• Experience with OpenAI (GPT-4), Claude, Mistral, LLaMA, or similar models.
• Knowledge of vector search, embedding models (e.g., BERT, Sentence Transformers), and semantic search techniques.
• Ability to build scalable AI workflows and deploy them via APIs or web apps (e.g., FastAPI, Streamlit, Flask).
• Familiarity with cloud platforms (AWS/GCP/Azure) and MLOps best practices.
• Excellent communication skills with the ability to translate technical solutions into business
impact.
Skills and Personal attributes we would like to have:
• Experience with prompt tuning, few-shot learning, or LoRA-based fine-tuning.
• Knowledge of data privacy and security considerations in GenAI applications.
• Familiarity with enterprise architecture, SDLC, or building GenAI use cases in regulated domains (e.g., finance, insurance, healthcare).
• Palantir Tool knowledge is preferred
(no sponsorship available)