76 to 100 of 125 Retrieval-Augmented Generation Jobs in the UK

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯

D and Industrial Chemistry. Knowledge of Pharma R&D and Commercial Chemistry processes, from synthetic to medicinal chemistry, is preferred. Familiarity with new technologies such as generative AI and RAG is essential to evolve our solutions. The role involves translating market trends into differentiated products with a focus on monetization, pricing, packaging, and market strategy. Responsibilities Vision and Strategy Develop More ❯

customer problems and technical possibilities. Have a deep understanding of the customer and new technical breakthroughs to truly challenge the future of natural language interfaces. Integrate and optimize LLM, RAG, and tool calling pipelines for text. Define requirements and run evaluations to optimize our technology stack to text based agents. Launch new channels into production. Work with clients and deployment More ❯

a founder or founding engineer - you know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external stakeholders Our values Trust More ❯

a founder or founding engineer - you know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external stakeholders Our values Trust More ❯

a founder or founding engineer - you know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external stakeholders Our values Trust More ❯

learning through iteration and feedback Understanding of enterprise data governance, AI model integration, and scalable data architecture Familiarity with AI/ML technologies, including GenAI models (e.g., OpenAI GPT, RAG, fine-tuning models, or machine learning frameworks) Good communication skills, especially when collaborating across different functions or surfacing potential risks or questions Familiarity with tools like JIRA, Confluence, Excel, or More ❯

AI - backed by a strong mathematics and statistics foundation. This includes hands-on experience with large language models (LLMs), foundation models, and autonomous agent frameworks, alongside expertise in implementing RAG systems, designing prompt engineering frameworks, and developing multi-agent systems. You should be proficient with both commercial and open-source technologies, including popular frameworks like LangChain, Hugging Face, and PyTorch … the AI/ML space by developing compelling technical content and practical implementations showcasing modern AI architectures. Create reference architectures, workshops, and demos that highlight integration patterns for LLMs, RAG systems, autonomous agents, and MLOps best practices. Share insights through AWS Blogs, public speaking events, and technical communities. - Build and nurture an internal AWS community of AI/ML experts … deep expertise in traditional machine learning and deep learning. Must have experience building production-grade AI systems, complemented by practical knowledge of modern Generative AI technologies (LLMs, foundation models, RAG systems) and autonomous agent frameworks. Strong background in AI architecture patterns and MLOps practices, with demonstrated ability to design and deploy enterprise-grade AI solutions at scale. PREFERRED QUALIFICATIONS - Experience More ❯

manage ontologies and robust knowledge representation frameworks. Integrate LLMs (Claude 3.7 or above, GPT, etc.) for content analysis, entity extraction, and semantic enrichment. Implement Retrieval Augmented Generation (RAG) and LLM-based fact extraction ([Subject, Verb, Object] triples). Build LLM-assisted QA pipelines for relationship validation and data consistency. Fine-tune and customise models … web technologies for structured data management. Required Skills & Expertise Graph engineering with Amazon Neptune and knowledge graph frameworks. Ontology engineering and semantic data modelling. LLM integration, fine-tuning, and RAG pipeline implementation. Advanced NLP and information retrieval methods. Experience with graph-based AI techniques (algorithms, GNNs, reasoning). Strong MLOps skills in performance evaluation and pipeline automation. Proficiency More ❯

paced environment. Hands-on with Next.js, Typescript (React/Node), Tailwind, SQL (Prisma), and Azure . Bonus points for experience with: LLM APIs (OpenAI, Anthropic), Retrieval Augmented Generation pipelines, Python, serverless architecture, or Microsoft Office Add-ins. Strong product mindset and passion for building. You may have tried a startup yourself or hacked together More ❯

we're looking for: Strong Python expertise with a track record of building complex systems. Previous experience in a leadership/mentoring capacity. Exposure to AI/ML applications (RAG, LLMs, NLP, or related areas is a bonus). Knowledge of modern cloud infrastructure (AWS/GCP/Azure) and DevOps practices. Passion for building, scaling, and making an impact More ❯

and medicinal chemistry to material science and chemical engineering, is desired.We are looking for a leader who is up to speed on new technologies, such as generative AI and RAG, and who would be able to rapidly evolve our solutions to incorporate those. This role is pivotal in translating market trends and customer needs into differentiated products, with a strong More ❯

use cases Site search solutions (e.g. Yext, Algolia, Elasticearch) Headless CMS/DXP platforms (Contentful, Sitecore, AEM etc) REST/GraphQL APIs Modern Web Development & Accessibility Analytics platforms LLMs, RAG, and Graph Database concepts You must be a natural storyteller who simplifies complexity, engages executives, and builds trust quickly. You have an ability to develop business cases that communicate requirements More ❯

then deploy novel techniques for all of the above What we're looking for Strong software engineer with fluency in Python and TypeScript Familiarity with building LLM apps, including RAG and prompting A daily LLM user in both coding and life Ability to pick the right problem and solve it Desire and curiocity to study basic financial stuff Why join More ❯

AI pipelines. What You'll Bring Deep solution architecture experience across cloud, data, and business platforms. Proven experience designing or integrating AI/GenAI solutions such as copilots, chatbots, RAG systems, or automation tools. Understanding of GenAI and agentic design patterns, including orchestration frameworks and RAG. Experience with event-driven systems, messaging platforms, and APIs. Familiarity with AI governance standards More ❯

Express , and modern front-end/back-end tooling. Strong Git/GitHub workflow knowledge (PR review, CI/CD, trunk-based development). Deep understanding of LLM fundamentals - RAG pipelines, embeddings, fine-tuning, tokenization, performance trade-offs. Broad knowledge of the LLM model landscape and practical prompting expertise across technical and creative tasks. Secure coding mindset and confidence working More ❯

goals into clear technical execution through strong communication and cross-team alignment. Nice to Have Experience with orchestration frameworks ( LangChain, LangGraph ) and multi-agent workflows Knowledge of vector databases, RAG pipelines, and lightweight model hosting Ability to design data pipelines and feedback loops for improving AI-driven features Awareness of emerging AI areas such as multimodal, edge AI, or AI More ❯

agents that drive measurable customer value. Key Responsibilities: Designing and improving agentic systems that break down complex tasks into actionable steps. Developing innovative context engineering strategies (e.g., retrieval-augmented generation, dynamic context construction, memory optimisation). Select, benchmark, and host LLMs on Amazon SageMaker or EKS , comparing and optimising models to ensure agentic systems More ❯

employee queries, policy navigation). Design and deploy conversational agents using LangChain and AutoGen for internal use. Build modular and scalable pipelines using LangGraph for multi-agent orchestration. Integrate RAG systems to enable context-aware document retrieval from internal HR/legal databases. Required Skills & Experience: Solid exposure in AI/ML engineering, with a strong focus on … NLP and LLMs. Proven hands-on knowledge with: LangChain, AutoGen, LangGraph and RAG Strong proficiency in Python and experience with frameworks like PyTorch or TensorFlow. Experience working with vector databases (e.g., FAISS, Weaviate, Pinecone). Familiarity with HR systems and data (Workday, SAP SuccessFactors, etc.) is a plus. Excellent communication skills and ability to translate technical concepts for non-technical More ❯

employee queries, policy navigation). Design and deploy conversational agents using LangChain and AutoGen for internal use. Build modular and scalable pipelines using LangGraph for multi-agent orchestration. Integrate RAG systems to enable context-aware document retrieval from internal HR/legal databases. Required Skills & Experience: Solid exposure in AI/ML engineering, with a strong focus on … NLP and LLMs. Proven hands-on knowledge with: LangChain, AutoGen, LangGraph and RAG Strong proficiency in Python and experience with frameworks like PyTorch or TensorFlow. Experience working with vector databases (e.g., FAISS, Weaviate, Pinecone). Familiarity with HR systems and data (Workday, SAP SuccessFactors, etc.) is a plus. Excellent communication skills and ability to translate technical concepts for non-technical More ❯

them for production, and putting the right evaluation and governance around them. What you'll do Build GenAI tools end-to-end (independently): chat/assistants, document Q&A (RAG), summarisation, classification, extraction, and workflow/agent automations. Own evaluation & safety : create offline/online eval sets, measure faithfulness/hallucination, bias, safety, latency and cost; add guardrails and red … ability to ship independently : from idea prototype secure, supportable production tool. Strong Python & SQL ; solid software engineering habits (testing, versioning, CI/CD). Practical LLM skills: prompt design, RAG , tool/function calling, evaluation & guardrails , and prompt/model observability. Sound grasp of statistics/experimentation (A/B tests, hypothesis testing) and communicating impact to non-technical audiences. More ❯

for AI engineering across the enterprise, ensuring alignment with organizational priorities and technology roadmaps. Architect for Scale : Drive architectural decisions for platforms and applications that leverage LLMs, retrieval-augmented generation, AI agents, and unstructured data processing at enterprise scale. Influence & Align : Partner with senior engineering leaders, product managers, business stakeholders, and governance teams to … Expertise in modern software architecture, distributed systems, and cloud infrastructure (AWS, Azure, or GCP). Deep hands-on experience integrating AI/ML systems into production environments, including LLMs, RAG, vector search, and AI agents. Strong ability to communicate technical strategy to executives, engineers, and business partners alike. Proven ability to set and enforce engineering standards for quality, security, and More ❯

Senior Gen AI Engineer – London (Hybrid) - £90-120k Prompt Engineering | RAG Pipelines/Vector DB | OpenAI & LangChain | Flexible Working Ada Meher is currently working with an equity backed SaaS software vendor in the construction space who are recruiting a Senior AI Engineer with skills in LLM, RAG pipeline builds & fine-tuning and optimisation of prompts & models to join their … Node/Typescript or similar Experience with relevant technologies such as OpenAI, LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer More ❯

its business milestones. Responsibilities (including but not limited to): Backend APIs (Python/FastAPI): Build reliable, secure services that power AI features and data retrieval at scale. RAG & vector search: Design, implement and iterate retrieval pipelines (chunking, embeddings, hybrid search, ranking, feedback loops). Own pgvector/Vector DB schemas, latency, relevance and cost. LLM integration … hardworking and dedicated, with an entrepreneurial/ownership mindset, strong communication skills and a team player 5+ years of professional experience in full-stack development. Hands-on experience with RAG systems , vector databases (pgvector/FAISS/Weaviate/ES k-NN), embeddings , and hybrid search (BM25 + vectors). Strong grasp of chunking strategies , metadata, indexing, recall/precision More ❯