Senior AI ScientistLondon – (Hybrid, 3 days a week in office)Up to £80,000 + benefitsAbout the RoleOur client is a data-led organisation that has made significant investment in modern data and AI capabilities. With a mature data platform and strong executive buy-in, they are now focused on embedding advanced AI into both customer-facing products and internal decision-making tools.As a Senior AI Scientist, you’ll play a key role in designing, building and deploying applied AI systems, with a particular focus on large language models (LLMs), retrieval-augmented generation (RAG), and conversational AI. This is a hands-on role operating across the full AI lifecycle, from experimentation and prototyping through to production deployment.You’ll work closely with product, engineering and business teams to ensure AI solutions are practical, scalable and deliver clear commercial and customer impact.Key ResponsibilitiesLeading the development of AI proof-of-concepts and production systems, including LLM-powered assistants and chatbots.Designing and implementing RAG pipelines using vector search, embeddings and knowledge retrieval strategies.Fine-tuning, evaluating and deploying language models, with a focus on response quality, reliability and performance.Building agentic workflows and conversational flows for both customer-facing and internal use cases.Defining evaluation frameworks and quality metrics for AI-generated outputs.Collaborating with product and engineering teams to integrate AI solutions into live systems.Contributing to AI architecture decisions and mentoring more junior engineers and data scientists.Your work will focus on delivering high-impact AI solutions, including:Customer and internal chatbots powered by LLMs.Intelligent knowledge retrieval and question-answering systems.Voice and chat-based automation to reduce cost-to-serve.AI tools that improve customer experience, retention and operational efficiency.What We’re Looking ForStrong experience building applied AI or machine learning systems in production environments.Advanced Python skills and hands-on experience with modern AI tooling (e.g. LLM frameworks, vector databases, orchestration libraries).Practical experience with LLMs, RAG architectures, embeddings and transformers.Experience deploying models using cloud infrastructure and working with microservices or APIs.Familiarity with MLOps concepts such as monitoring, evaluation and continuous improvement.Strong communication skills and the ability to translate business problems into AI-driven solutions.A collaborative mindset and experience working with both technical and non-technical stakeholders.If this role looks of interest, apply below.