101 to 125 of 186 Retrieval-Augmented Generation Jobs in the UK

solutions Actively coding, building and solving complex backend challenges What do I need? Demonstrable experience of building and deploying complex AI applications using LLMs, RAG and agentic workflows GraphRAG/Knowledge Graph expertise - deep knowledge with Knowledge Graphs and vector databases Strong technical fluency - TypeScript/React, Langgraph/Langchain ...

solutions Actively coding, building and solving complex backend challenges What do I need? Demonstrable experience of building and deploying complex AI applications using LLMs, RAG and agentic workflows GraphRAG/Knowledge Graph expertise - deep knowledge with Knowledge Graphs and vector databases Strong technical fluency - TypeScript/React, Langgraph/Langchain ...

solutions Actively coding, building and solving complex backend challenges What do I need? Demonstrable experience of building and deploying complex AI applications using LLMs, RAG and agentic workflows GraphRAG/Knowledge Graph expertise - deep knowledge with Knowledge Graphs and vector databases Strong technical fluency - TypeScript/React, Langgraph/Langchain ...

machine learning, and mathematical concepts Proven experience delivering production-grade data science solutions Hands-on experience with modern data science tooling, including LLMs/RAG approaches Advanced SQL skills Experience working in complex, enterprise-scale environments Strong written and verbal communication skills Ability to perform under technical interviews including live ...

Engineering skills (3 years+) Developed LLM architecture and deployed LLM applications Uptodate with current trends in AI Some experience with applying latest techniques like RAG architecture, GenAI, Parallel training etc The role is hybrid, with adhoc requirements to be on client premises (London) this could be between 1-3 days ...

based applicants only) You won't be "just another engineer." You'll join a small, high-impact team shaping the platform behind LLMs, RAG applications, and internal AI tools. Your work will influence how the organisation uses AI at scale, giving you ownership few roles can match. Your Mission Build ...

core mathematical concepts Proven experience delivering production-grade data science solutions in complex environments Exposure to modern data science tooling, including LLMs and RAG-style approaches Advanced SQL skills Experience working at enterprise scale, ideally in trading or similarly data-intensive domains Strong written and verbal communication skills Comfortable with ...

build for impact, not just for the hype. Self-Taught & Curious: You didn't wait for a corporate training module to learn about RAG, agents, or fine-tuning. You learned it because you couldn't help yourself. What You'll Do Design and deploy AI-driven features that solve real ...

/experience/knowledge: Experience in architecting and solutioning in Gen AI, Agentic AI, classic ML, and automation space. Good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability. Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face ...

technical stakeholders GenAI work You will work with GenAI in ways that are grounded in real use cases and business value: Building RAG systems that improve search, content discovery or productivity rather than existing for their own sake Implementing guardrails so models do not leak PII or generate harmful … Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone or Weaviate Ability to evaluate and improve prompts and retrieval quality using clear metrics Understanding ...

Exceptional communication and stakeholder engagement skills. Preferred Skills Expertise in prompt engineering, context management, and fine-tuning techniques (LoRA/QLoRA). Experience building RAG pipelines and agentic workflows for enterprise financial applications. Knowledge of vector databases (FAISS, Pinecone, Weaviate) and graph technologies (Neo4j). Familiarity with observability tools … containerization and cloud deployment (Docker, AWS, Azure). Tools & Platforms GitHub Copilot, ChatGPT/GPT-4, Claude for AI-assisted development. LangChain, LlamaIndex for RAG implementations. SonarQube for code security and quality assurance. Jira with AI plugins for agile delivery. Monitoring tools like Datadog and Dynatrace for production reliability. Ideal ...

systems: From prototypes to scaled deployments, you'll design and ship systems that power real-world experiences: personalised journeys, churn prediction, lead scoring, RAG-based knowledge assistants, energy optimisation, operational AI tools, and more. Data, modelling & experimentation: Analyse and prepare data for AI use-cases: consumer behaviour, CRM interactions, digital … techniques, including: Classical Machine Learning, e.g. regression, tree-based methods, uplift models, time-series forecasting, LLM and generative AI, e.g. prompt engineering, retrieval-augmented generation, fine-tuning or adapters where appropriate. Design and implement prediction models for use cases like churn, propensity, next best ...

designing, planning, prototyping, productionizing, maintaining, and documenting reliable and scalable data science products in complex environments. Applied knowledge of data science tools (LLMs/RAG etc.) and approaches across all data lifecycle stages. Advanced SQL knowledge. Customer-centric and pragmatic mindset. Focus on value delivery and swift execution, while maintaining ...

integrating AI into existing systems. To be considered for this role you’ll possess the following skills and experience: - Practical experience implementing AI or RAG-type integrations - Solid .NET, Python, C# skills with a good understanding of SQL - Familiarity with Azure AI services, DevOps pipelines and containers - Some awareness ...

Role Description: Gen-AI Practitioner: 5-8 years of hands-on experience in data science or engineering, with strong delivery across Gen-AI workflows (RAG, prompt engineering, open-source LLMs, LangChain, etc.). EMEA Project Leadership: Drives AI projects in operational areas like Corporate Trust and International AI Champions, with ...

through to production. Work with messy, large-scale structured and unstructured data to build models that move real business metrics. Lead the design of RAG-style architectures (embeddings, dense search) and advanced NLP/NLU applications. Partner closely with pre-sales and delivery teams to scope opportunities and shape solution ...

Security/Compliance (HIPAA, GDPR, NHS DTAC, SOC2) What You’ll Do Build highly scalable backend services and APIs Work on LLM-powered features (RAG pipelines, embedding workflows) Develop agentic document extraction systems for clinical PDFs Own backend reliability, performance, and security Work cross-functionally with product, frontend, and leadership ...

with React for front-end development. Understanding of modern CI/CD, containerisation, and observability. Exposure to AI/ML tooling, vector databases, embeddings, RAG workflows, or LLM integration is a plus. About You ~5+ years in software engineering with solid full-stack experience. Comfortable owning technical work ...

with streaming and batch pipelines and exposure to the tools that support them Confidence designing infrastructure to support modern data science workflows, including GenAI, RAG pipelines, and ML inference Good understanding of data privacy, retention, and access control frameworks in regulated environments A collaborative mindset, with experience working across business ...

models. Strong experience with transformer models, embeddings, vector search, and Generative AI architectures. Practical experience using Hugging Face, OpenAI, LangChain, Retrieval Augmented Generation, and diffusion models. Strong MLOps experience including MLflow, Kubeflow, Airflow, Docker, Kubernetes, and CI and CD tooling. Strong programming skills … teams and enterprise environments. Desirable Skills Experience with vector databases such as FAISS, Pinecone, or Weaviate. Knowledge of enterprise scale Retrieval Augmented Generation systems. Experience within global manufacturing, engineering, or industrial environments. Understanding of data governance, security, and responsible AI principles. What This Role ...

grade AI solutions across various sectors, predominantly financial services. You will work on a diverse mix of projects, ranging from complex Generative AI architectures (RAG, Agents) to Machine Learning problems (forecasting, classification, anomaly detection). The ideal candidate will have strong software engineering fundamentals alongside deep ML knowledge. Role: Architect … Build GenAI Systems: Design and deploy advanced RAG and GraphRAG applications ML Implementation: Develop and maintain traditional machine learning models (Regression, Classification, Clustering, Time-Series) to solve structured data problems Agentic Workflows: Build multi-agent systems using frameworks like LangGraph, AutoGen, or similar to automate complex analysis tasks Evaluation & Observability ...

MLOps practices: CI/CD, model monitoring, retraining. * Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). * Generative AI features: embeddings, RAG, AI agents. * Clean, testable code with modern engineering practices. * Align with enterprise architecture and governance. * Collaborate with architects and stakeholders. * Lifecycle management of models. * Pilot … plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. * Strong coding, communication, and collaboration skills. * Strategic thinking, problem-solving, and stakeholder engagement. In accordance with the Employment Agencies ...

engineering experience in product focused environments A track record of taking features or products from idea to launch Experience building with LLMs including RAG, semantic search and vector stores Hands on with TypeScript, React, Node and Python Comfortable deploying to AWS, GCP or Azure Curious, fast, accountable and motivated ...

Ability to build responsive interfaces and manage complex backend logic. Excellent problem-solving and time management skills. Bonus Skills: Experience with Azure, Docker, LangChain, RAG models, and pandas. What's on Offer Transparent salary framework and twice-yearly promotion opportunities. 25 days annual leave plus three extra days at Christmas. ...

Centre of Excellence within a consultancy or Microsoft Partner environment. Deep expertise in enterprise AI solution design, including generative AI, Azure OpenAI, Copilot Studio, RAG, cognitive services, and data orchestration patterns. Strong background in the Microsoft ecosystem, especially Dynamics 365, Power Platform, and Azure services. Hands‐on experience shaping ...