Data Scientist (Generative AI)
London, United Kingdom (Flexible hybrid working)
Permanent
ROLE SUMMARY
We are seeking a highly capable and innovative Data Scientist with specialized experience in Generative AI to join our Data Science Team. In this role, you will lead the development and deployment of enterprise-grade GenAI solutions, including LLM-based applications, prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG).
The ideal candidate possesses a strong foundation in machine learning and NLP, paired with hands-on experience using modern GenAI frameworks and cloud environments.
KEY RESPONSIBILITIES
- Design & Build: Develop Generative AI solutions using Large Language Models (LLMs) across enterprise use cases such as customer service, document automation, summarization, and knowledge retrieval.
- Model Adaptation: Fine-tune and adapt foundation models using domain-specific datasets.
- Pipeline Architecture: Implement RAG pipelines, embedding models, and vector databases (e.g., FAISS, Pinecone, ChromaDB).
- Cross-Functional Collaboration: Partner with data engineers, MLOps, and product teams to build end-to-end AI applications and APIs.
- Prompt Engineering: Develop custom prompts and prompt chains utilizing frameworks like LangChain, LlamaIndex, PromptFlow, or custom code.
- Optimization & Governance: Evaluate model performance, mitigate bias, and optimize accuracy, latency, and operational cost.
- Innovation: Stay current with the latest advancements in LLMs, transformers, and GenAI system architecture.
Essential Qualifications
- 5+ years of professional experience in Data Science / ML, with 1+ year of hands-on delivery in LLM / GenAI projects.
- Strong Python Skills: Expertise with libraries such as Transformers, LangChain, scikit-learn, PyTorch, or TensorFlow.
- LLM Expertise: Direct experience working with foundation models such as OpenAI (GPT-4), Claude, Mistral, LLaMA, or similar architectures.
- Semantic Search & Embeddings: Deep understanding of vector search, embedding models (e.g., BERT, Sentence Transformers), and retrieval techniques.
- Deployment: Demonstrated ability to build scalable AI workflows and deploy them via APIs or web frameworks (e.g., FastAPI, Streamlit, Flask).
- Cloud & MLOps: Familiarity with major cloud providers (AWS, GCP, Azure) and MLOps best practices.
- Communication: Exceptional ability to translate complex technical solutions into strategic business impact.
Desirable Skills
- Hands-on experience with prompt tuning, few-shot learning, or LoRA-based fine-tuning.
- Practical knowledge of data privacy, security, and governance considerations within GenAI systems.
- Familiarity with enterprise architecture, SDLC, or building solutions within highly regulated domains (finance, healthcare, insurance).
- Knowledge of Palantir tools is a strong bonus