26 to 46 of 46 Retrieval-Augmented Generation Jobs in the South East

Python and JavaScript Hands-on experience with backend frameworks and modern frontend frameworks Deep knowledge of Generative AI and agent-based AI frameworks including RAG and orchestration patterns Strong experience working with vector databases and AI-specific data stores Solid understanding of AI and ML models, LLMs and generative ...

Process: 1. Virtual Interview2. Virtual (technical) interview3. In-person interview KEYWORDS Data Scientist, Data Science, Machine Learning, Artificial Intelligence, AI, AI Engineer, LLM, RAG, Langchain. ...

artificial intelligence, and machine learning, with considerable in-depth knowledge in implementing solutions using AWS. Practical cloud-native experience, specifically with AWS. Experience with RAG-based semantic search and LLM inference workflows using OpenAI and Claude models. ...

Process: 1. Virtual Interview2. Virtual (technical) interview3. In-person interview KEYWORDS Data Scientist, Data Science, Machine Learning, Artificial Intelligence, AI, AI Engineer, LLM, RAG, Langchain. ...

Process: 1. Virtual Interview2. Virtual (technical) interview3. In-person interview KEYWORDS Data Scientist, Data Science, Machine Learning, Artificial Intelligence, AI, AI Engineer, LLM, RAG, Langchain. ...

Drug Discovery, Biomedical Research or similar with an ability to interpret scientific literature Experience or strong interest in NLP and LLM technologies (transformers, RAG, prompt engineering) Python and AI frameworks for text analytics Knowledge of ontologies, knowledge graphs and semantic technologies Confident communicator who enjoys working with diverse stakeholders Curious … Information Science, Artificial Intelligence AI, Natural Language Processing (NLP), Large Language Models (LLMs), Machine Learning, Text Analytics, Literature Mining, Retrieval Augmented Generation (RAG), Prompt Engineering, Python, Knowledge Graphs, Ontologies, Semantic Technologies, London, Slough, Reading, Oxford, VRS9353SE ...

observability). Demonstrated success in leading teams Deep understanding of AI/ML solutions implementation, with emphasis on LLM applications and retrieval-augmented generation. Expertise in modern SDLC and DevOps: CI/CD (e.g., Jenkins/GitLab), infrastructure as code (e.g., Terraform/CloudFormation), automated testing ...

TypeScript-based applications Understanding of semantic/graph data (e.g., RDF triples, SPARQL , Amazon Neptune ) Ability to validate AI-supported features, including LLM/RAG behaviours via Amazon Bedrock Knowledge of performance testing for serverless architectures and WCAG 2.2 AA accessibility Familiarity with GDS Standards and testing in the public ...

strategy, solution design, and delivery. You will: Lead discovery and design of AI-led solutions aligned to business outcomes Architect and scale GenAI, RAG, and Agentic AI systems in production Advise senior stakeholders on AI strategy, feasibility, and roadmap Act as a technical authority during bids, proposals, and client workshops … client presentations with clear, credible technical direction Solution Architecture & Delivery Design enterprise-ready Data & AI architectures using modern cloud platforms (AWS, Azure, GCP) Architect RAG-based GenAI solutions, chatbots, and autonomous/agentic workflows Select appropriate models, frameworks, orchestration tools, and evaluation approaches Ensure solutions are secure, scalable, observable ...

Software Engineer candidate will have: · A deep experience of working on backend projects with Typescript and Node · Strong experience with Python/LLMs and RAG · Experience of building products that customers will love to use · Experience of working in a small team and getting things done ...

solutions that improve operational efficiency, regulatory compliance, and customer trust while leveraging advanced analytics and automation. Your Responsibilities Design, develop, and optimize RAG-based solutions integrating retrieval systems with large language models. Build and maintain applications using LangChain to orchestrate complex AI workflows. Implement and manage vector databases … scalable AI-powered applications. Fine-tune and evaluate large language models for specific use cases and domain adaptation. Optimize retrieval and generation workflows for performance, accuracy, and cost-efficiency. Stay updated on the latest advancements in generative AI, retrieval techniques, and vector search technology. ...

What You’ll Work On Designing and implementing Agentic AI architectures for enterprise workflows. Building and deploying LLM-powered solutions, including Q&A systems, RAG pipelines, and autonomous agents. Developing custom copilots, plugins, and declarative agents using Microsoft Copilot Studio and Azure AI Agent Service. Operationalising AI solutions using Microsoft … Python and deep learning/NLP libraries (LangChain, HuggingFace, PyTorch, TensorFlow). Experience deploying AI workloads on Azure (ACA, AKS, Azure Functions) and implementing RAG with Azure AI Search. Ability to engage with clients, translate business needs, and deliver scalable end-to-end AI solutions. Why Join: Work directly with ...

Engineer, you’ll be responsible for designing, building, and deploying production-grade AI systems in complex enterprise environments. You will: Build and deploy GenAI, RAG, and Agentic AI systems that solve real business challenges Develop robust pipelines, services, and integrations that turn models into usable products Work across front … product-driven engineering where you ship real systems into production. Key Responsibilities: AI System Development Build and optimise AI models and pipelines Implement RAG, agentic workflows, and advanced reasoning techniques Deploy LLM-driven features into real products Full-Stack & Platform Engineering Develop APIs, backend services, data flows, and integration layers ...

Agentic AI solutions aligned to real business workflows Build and orchestrate autonomous agents with planning, reasoning, and tool use Develop LLM-driven systems including RAG, Q&A, and enterprise copilots Integrate GenAI into existing platforms, products, and processes Own delivery end-to-end: design through to production Work closely with … Generative AI and LLMs Agentic AI frameworks such as LangGraph, AutoGen, or CrewAI Python with modern AI libraries (LangChain, Hugging Face, PyTorch) Experience building RAG pipelines and knowledge retrieval systems Cloud platforms: Azure, AWS, or GCP Microsoft/Azure (Highly Desirable) Microsoft AI Foundry Azure AI Agent Service ...

Responsibilities Data & Retrieval Build robust ingestion pipelines for PDFs/Word/Excel/Audio/JSON and semi-structured sources. Design RAG systems: chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search … tracing/telemetry (OpenTelemetry, LangSmith), metrics, logs, cost and token usage monitoring, alerts. Apply evaluation & QA: regression suites, offline eval sets/golden data, RAG evals (faithfulness, answer relevance, citation correctness), A/B tests, win-rate testing. Ensure reliability: rate-limit handling, retries/backoff, idempotency, circuit breakers, caching ...

that enhance global sales workflows. Build scalable backend services and full-stack applications for internal teams. Architect, implement, and optimize LLM orchestration frameworks and RAG-based solutions. Collaborate with cross-functional teams to define requirements and deliver innovative tools. Ensure reliability, scalability, and performance of distributed systems handling massive datasets. … workflows . Strong proficiency in Angular (primary frontend); React optional. Hands-on experience with LangChain, LangGraph, and Indexify , with a deep understanding of RAG architectures . Expert-level SQL and experience with BigQuery and vector databases (Pinecone, Weaviate, Vertex AI Search). Proven track record in MLOps and deploying ...

Skills for the Artificial Intelligence Architect: Proven experience architecting solutions across Generative AI, Agentic AI, machine learning and automation Strong understanding of prompt engineering, RAG pipelines, model tuning, MLOps and LLMOps Experience delivering enterprise-scale RAG solutions using LLMs and vector databases Ability to design AI platforms that integrate with ...

best practices for content analytics. Core Skills & Competencies . Technical Skills: o Experience or interest in NLP and LLM applications (transformers, Retrieval-Augmented Generation, prompt engineering) o Familiarity with data visualization and scientific communication tools o Knowledge graph and ontology management o Python ...

depth knowledge in implementing solutions using AWS. Deep understanding of AI/ML solutions implementation, with emphasis on LLM applications and retrieval-augmented generation. Practical cloud-native experience, specifically with AWS (compute, storage, networking, IAM, KMS, observability). Experience with RAG-based semantic search ...

months IR35: Outside Location: London (onsite once per month ) Start: ASAP What you’ll work on Building Agentic AI workflows and automation Designing RAG pipelines for insurance data Developing LLM-powered tools using Python , LangChain , LlamaIndex , and vector databases Evaluating, fine-tuning, and integrating LLMs (OpenAI/Anthropic/Mistral … Data Science & Engineering teams to productionise models What you’ll need Strong Data Science + AI Engineering background Hands-on experience with Agentic AI , RAG , LLMs , LangChain , Python Knowledge of vector search, embeddings, and scalable pipelines Experience working in enterprise or regulated environments Must have insurance domain experience Insurance domain ...

until at least February 2027. What you'll need to succeed Experience or strong interest in NLP, LLMs and AI applications such as transformers, RAG or prompt engineering, preferably gained within the pharmaceutical, biotech, medtech or biomedical space Familiarity with Python and modern AI frameworks for text analytics, alongside data … content, analytics, biomedical, literature, mining, summarisation, machine, learning, data, ontology, metadata, FAIR, knowledge, graphs, information, retrieval, Python, text, analytics, RAG, drug, discovery, development, transformers, semantic, technologies, visualisation, scientific, workflows, biopharma ...