Data Scientist

TechYard; Greater London; Hybrid; GBP 90,000 - 140,000; Full time

Job summary

TechYard in London seeks a Data Scientist (Generative AI) to lead design, development and deployment of GenAI solutions leveraging LLMs and retrieval techniques. You will work with cutting-edge platforms to deliver enterprise-grade AI applications.

The role requires hands-on ML/NLP expertise, experience with OpenAI, LangChain, Hugging Face, and cloud ML platforms, plus strong collaboration with engineering and product teams.

Qualifications

• 5+ years in Data Science and ML, with GenAI delivery.
• Strong Python programming skills and ML libraries.
• Hands-on experience with OpenAI GPT, Claude, or similar models.
• Solid vector search and embedding techniques.
• Experience deploying AI via APIs or web apps (FastAPI, Streamlit).
• Cloud exposure (AWS/Azure/Google Cloud) and MLOps.
• Excellent communication and stakeholder skills.

Responsibilities

• Design, develop, and deploy GenAI solutions using LLMs.
• Fine-tune foundation models on domain data.
• Build RAG pipelines with embeddings and vector databases.
• Collaborate with Data Engineering, MLOps, and Product teams.
• Design prompts and workflows with LangChain, LlamaIndex, PromptFlow.
• Evaluate and monitor models for accuracy, latency, cost.

Skills

Data Science

Machine Learning

Python

NLP

LLMs

Tools

Transformers

LangChain

scikit-learn

PyTorch

TensorFlow

Job description

• Role Title: Data Scientist (Generative AI)
• Business Unit/Segment: Data Management
• Location: London, United Kingdom (Flexible Hybrid Working)
• Employment Type: Permanent

About the Role

We are seeking a talented and forward-thinking Data Scientist with expertise in Generative AI to join our Data Science team. In this role, you will lead the design, development, and deployment of innovative GenAI solutions that solve real business challenges. You will work with cutting-edge technologies, including Large Language Models (LLMs), prompt engineering, fine-tuning, embeddings, and Retrieval-Augmented Generation (RAG), to deliver scalable, enterprise-grade AI applications.

The ideal candidate will have a strong background in machine learning and natural language processing (NLP), together with hands-on experience using modern GenAI frameworks and platforms such as OpenAI, LangChain, Hugging Face, Vertex AI, Amazon Bedrock, or similar technologies.

Key Responsibilities

As a Data Scientist, you will

Design, develop, and deploy Generative AI solutions powered by Large Language Models (LLMs) to address business challenges across areas such as customer service, document automation, summarisation, and knowledge retrieval

• Fine-tune and adapt foundation models using domain-specific datasets to improve performance and business relevance


• Build and optimise Retrieval-Augmented Generation (RAG) pipelines using embedding models and vector databases such as FAISS, Pinecone, or ChromaDB


• Collaborate with Data Engineering, MLOps, and Product teams to develop and deploy end-to-end AI applications and APls


• Design and optimise prompts and prompt workflows using tools such as LangChain, LlamaIndex, PromptFlow, or equivalent frameworks


• Evaluate model performance, monitor quality, mitigate bias, and optimise solutions for accuracy, latency, scalability, and cost


• Stay current with the latest developments in LLMs, transformer architectures, and the rapidly evolving Generative AI landscape

Essential Skills and Experiance

To be successful in this role, you will have

• 5+ years' experience in Data Science and Machine Learning, including at least 1 year of hands-on experience delivering LLM or Generative AI solutions


• Strong Python programming skills, with experience using libraries such as Transformers, LangChain, scikit-learn, PyTorch, or TensorFlow


• Hands-on experience working with models such as OpenAI GPT, Claude, Mistral, Llama, or similar foundation models


• A solid understanding of vector search, embedding models (e.g. BERT, Sentence Transformers), and semantic search techniques


• Experience building scalable AI solutions and deploying them through APIs or web applications using frameworks such as FastAPI, Streamlit.


• Experience working with cloud platforms (AWS, Azure, or Google Cloud) and familiarity with MLOps principles and best practices


• Excellent communication and stakeholder management skills, with the ability to translate complex technical concepts into clear business outcomes

Desirable Skills and Expereince

The following would be advantageous

• Experience with prompt tuning, few-shot learning, LoRA, or other parameter-efficient fine-tuning techniques


• Understanding of data privacy, security, and responsible AI considerations when developing Generative AI applications


• Experience delivering AI solutions within enterprise environments, with knowledge of software development lifecycle (SDLC) practices and enterprise architecture


• Experience working in regulated industries such as financial services, insurance, or healthcare