51 to 75 of 217 Retrieval-Augmented Generation Jobs in the UK

discuss flexible working!Top 3 skills needed for this role: Expert AI/ML Skills – Strong in both traditional ML and modern LLM/RAG/agentic workflows Production Deployment & MLOps – Comfortable shipping scalable AI systems using APIs, microservices, Docker/Kubernetes, and cloud (GCP ideal) Business Partnering – Able … based applications. The role involves building internal and external AI solutions such as Retrieval-Augmented Generation (RAG) systems, chatbots, and voice-based customer service platforms, all deployed on Google Cloud Platform (GCP). You will work closely with data scientists, software engineers, and product ...

intersection of DevOps, cloud infrastructure and applied AI owning the full lifecycle of Retrieval-Augmented Generation (RAG) and LLM-powered systems across modern cloud architecture. This role is about engineering, not research. You will architect and run the infrastructure that enables AI to perform … hardened, production-ready services while shaping a reusable AI platform capable of supporting multiple products. What You'll Be Doing Design and optimise scalable RAG pipelines and vector search systems Orchestrate multi-model AI services with a focus on latency, resilience and performance Productionise GenAI workflows and ensure they operate ...

automate business processes end-to-end Developing Generative AI workflows using Azure OpenAI and Retrieval-Augmented Generation (RAG) Building and deploying automation solutions using Power Automate and Power Apps Integrating AI solutions with enterprise systems (CRM, M365, APIs, data platforms) Ensuring solutions are secure … data protection principles Ability to translate business requirements into scalable technical solutions Strong communication skills and experience working with stakeholders Desirable Experience with RAG pipelines and LLM-based solutions Exposure to Microsoft Copilot Studio Experience with Databricks, SQL or Python Knowledge of CI/CD and DevOps practices Background working ...

play a key role in building intelligent, scalable features, leveraging the latest in LLMs, retrieval-augmented generation (RAG), and cloud-native technologies. There are multiple roles available, ranging from mid-level to senior engineers, offering opportunities to both deepen technical expertise and take ownership … initiatives. What You’ll Be Doing Design and build AI-powered features using modern LLM frameworks Develop and optimise RAG pipelines (vector databases, embeddings, retrieval strategies) Integrate AI services into existing backend systems and APIs Work with structured and unstructured data to improve model outputs Collaborate with cross ...

play a key role in building intelligent, scalable features, leveraging the latest in LLMs, retrieval-augmented generation (RAG), and cloud-native technologies. There are multiple roles available, ranging from mid-level to senior engineers, offering opportunities to both deepen technical expertise and take ownership … initiatives. What You’ll Be Doing Design and build AI-powered features using modern LLM frameworks Develop and optimise RAG pipelines (vector databases, embeddings, retrieval strategies) Integrate AI services into existing backend systems and APIs Work with structured and unstructured data to improve model outputs Collaborate with cross ...

have 'under-the-hood' knowledge of Python, specifically for building high-throughput microservices and complex data pipelines using FastAPI, Pandas, and NumPy . The RAG & Agentic Expert: This is the 'Critical' requirement. Our client needs someone with deep hands-on experience building Retrieval-Augmented Generation (RAG) pipelines and Agentic frameworks . You should know how to use LangChain or LlamaIndex to create AI that can execute multi-step tasks. The Data Architect: Proficiency in Vector Databases is essential. You should be comfortable designing data persistence layers using PG Vector, Pinecone, Milvus, or Mongo ...

Platform. Strong understanding of responsible AI principles, including explainability, fairness, and ethical deployment. Familiarity with retrieval-augmented generation (RAG), vector databases, and knowledge graphs. Ability to work effectively across technical and non-technical teams, with strong communication and collaboration skills. Awareness of data governance ...

Platform. Strong understanding of responsible AI principles, including explainability, fairness, and ethical deployment. Familiarity with retrieval-augmented generation (RAG), vector databases, and knowledge graphs. Ability to work effectively across technical and non-technical teams, with strong communication and collaboration skills. Awareness of data governance ...

managing multi-API workflows using tools like LiteLLM to ensure flexibility and resilience. Building sophisticated Retrieval-Augmented Generation (RAG) systems, leveraging advanced techniques like embeddings with Voyage AI, rerankers , and query enrichment . Designing and maintaining efficient data pipelines and vector storage solutions using … PyTorch. Proven experience building and deploying applications with LLM APIs such as OpenAI , Anthropic , Gemini , and DeepSeek . Hands-on experience with the full RAG pipeline, including vector embeddings , rerankers , and data indexing in databases like MongoDB. Practical knowledge of LLM fine-tuning, prompt engineering, and performance optimization. Familiarity with ...

investment banking, asset management, or capital markets domains.* Deep expertise in ML, NLP, LLMs, retrieval-augmented generation (RAG), embeddings, and modern MLOps practices.* Strong experience working with both structured financial datasets and unstructured data sources (e.g., filings, call transcripts, research).* Familiarity with front ...

gatherings Salary: Senior: £100–£130k base + growth trajectory Principal: £130–£170k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We’re looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

just proofs of concept. AI Engineer Responsibilities Own and evolve a production-ready AI platform Design and deliver AI-powered client solutions, including RAG pipelines, agent architectures, LLM integrations, and protocol-driven tooling Build scalable AI systems using modern software engineering best practice (clean code, testing, CI/CD) Develop … experience with Large Language Models (LLMs), including API integrations, prompt engineering, agent workflows, and Retrieval-Augmented Generation (RAG) systems Experience shipping AI-powered features into production environments Proficiency in at least two modern programming languages Familiarity with emerging AI tooling (e.g. MCP, A2A, Threads ...

production. Hands‐on experience with: Python (PyTorch, TensorFlow, scikit‐learn) Data engineering tools (SQL, Spark, Airflow or similar) LLMs, NLP, and document intelligence (e.g. RAG, embeddings) Experience with cloud platforms (AWS, Azure, or GCP). Understanding of MLOps , CI/CD, model monitoring, and governance. M&A/Transaction Experience ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

challenges into scalable AI solutions Facilitate discovery sessions with legal professionals to identify high-value opportunities Evaluate and implement AI tools (e.g. LLMs, NLP, RAG pipelines) in production environments Ensure solutions meet high standards for accuracy, confidentiality, and risk Drive adoption through intuitive user experiences and clear value articulation Define …/ML-powered products Strong understanding of: Large Language Models (LLMs) NLP, classification, extraction, summarisation Retrieval-Augmented Generation (RAG) or similar architectures Experience working in regulated environments (e.g. legal, financial services, healthcare) Strong Agile delivery experience working with cross-functional teams Highly desirable Experience ...

challenges into scalable AI solutions Facilitate discovery sessions with legal professionals to identify high-value opportunities Evaluate and implement AI tools (e.g. LLMs, NLP, RAG pipelines) in production environments Ensure solutions meet high standards for accuracy, confidentiality, and risk Drive adoption through intuitive user experiences and clear value articulation Define …/ML-powered products Strong understanding of: Large Language Models (LLMs) NLP, classification, extraction, summarisation Retrieval-Augmented Generation (RAG) or similar architectures Experience working in regulated environments (e.g. legal, financial services, healthcare) Strong Agile delivery experience working with cross-functional teams Highly desirable Experience ...

services Contribute to the development and evolution of a production-grade AI platform Build and integrate AI-powered features such as LLM-driven services, RAG pipelines, and agent-based workflows into core systems Apply modern software engineering best practices (clean code, testing, CI/CD, observability) to all solutions Develop … Desirable: Experience integrating AI into production systems (e.g. chat interfaces, automation, intelligent search) Familiarity with Retrieval-Augmented Generation (RAG), vector databases, or embeddings Experience with cloud platforms (AWS preferred) Awareness of emerging AI tooling (e.g. MCP, A2A, Threads or similar) Open-source contributions ...

features-from data ingestion and feature engineering to model training, evaluation, and deployment Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings, vector search, caching, re ranking) Optimise training and inference performance via batching, quantisation, distillation, LoRA/PEFT, accelerator utilisation (GPU/… building GenAI applications using Python Hands-on Experience with API Development and Microservices architecture and End to End integrations Knowledge of RAG (Retrieval-Augmented Generation) and ADK, MCP Solid understanding of LLMs, prompt engineering, and graph-based workflows Hands-on Experience with API Development ...

About the Role We are seeking a Senior Full-Stack Engineer to join our team and help build the next generation of AI solutions. This is a unique opportunity to be a key technical contributor in a fast-paced, innovative environment where you'll wear many hats … trade-offs Experience with prompt engineering techniques — few-shot prompting, chain-of-thought, structured output, and instruction tuning Practical knowledge of RAG (Retrieval-Augmented Generation) architectures, including chunking strategies, vector embeddings, and similarity search Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex ...

deploy Generative AI solutions using large language models (LLMs) Develop and optimise prompt engineering strategies for production use cases Build end-to-end pipelines (RAG, embeddings, vector search) for scalable GenAI applications Work with both structured and unstructured datasets, including text-heavy data Collaborate closely with data engineering and software ...

hands on experience in deploying and hosting Large Foundational Models. Experience with LLM architecture (e.g., Transformer, GANs, VAEs), Fine-tuning, Retrieval Augmented Generation techniques and Context embedding, Vector database technologies and Semantic Search techniques & tools. Experience in writing robust and efficient code in Python. ...

hands on experience in deploying and hosting Large Foundational Models. Experience with LLM architecture (e.g., Transformer, GANs, VAEs), Fine-tuning, Retrieval Augmented Generation techniques and Context embedding, Vector database technologies and Semantic Search techniques & tools. Experience in writing robust and efficient code in Python. ...

deploying models in production and building scalable AI software. Deep understanding of advanced modelling methods: deep learning, GNNs, computer vision, sensor fusion, vector embeddings, RAG, generative AI . Experience designing AI/ML architectures and providing strategic technical guidance to enterprise clients. Proficiency with cloud compute platforms for training … impact. Apply advanced AI techniques—including deep learning, classical ML, graph neural networks, object detection, sensor fusion, vector embeddings, retrieval‐augmented generation (RAG), and generative AI models —to design systems that learn, adapt, and operate autonomously. Provide strategic architectural guidance on AI systems, ensuring ...

healthtech Deployed LLMs or predictive models in production environments Familiarity with FHIR, HL7, or secure health data pipelines Hands-on LLM work: prompt engineering, RAG, fine-tuning (LoRA/QLoRA) The Package Competitive salary + meaningful equity Work directly with and be mentored by two highly experienced co-founders Spacious ...

cloud-native development patterns. Solid understanding of security best practices, IAM, secrets management, and artifact governance. Nice to have: Experience with vector databases, RAG pipelines, or multi-agent AI systems. Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK). Hands-on understanding of model drift detection, A/ ...