1 to 25 of 46 Retrieval-Augmented Generation Jobs in the South East

Stay up-to-date with new technologies and best practices in data engineering, advancements in generative AI, transformer architectures, and retrieval-augmented generation (RAG) techniques. Ensure the data security standards are met, in conjunction with the Information Security team Manage AI/ML projects and mentor junior team members Experience: Extensive experience in data More ❯

AI expert ready to take your skills to the next level? Do words like Azure OpenAI, Cognitive Services, prompt engineering, Retrieval-Augmented Generation (RAG) architectures, vector stores, and API integrations make you light up inside? If so, we want to hear from you! At 1st Central , we're on an exciting journey with AI … develop AI and Generative AI solutions using services like Azure OpenAI and Azure Cognitive Services Implement prompt engineering techniques and Retrieval-Augmented Generation (RAG) architectures. Ensure scalability, security, auditability, and efficiency of AI solutions through detailed system design and development practices. Deploy and manage AI solutions via CI/CD pipelines in Azure DevOps … deploying, and managing production-grade AI and Generative AI systems. Extensive experience with Cloud-based AI and Cognitive Services, and Retrieval-Augmented Generation (RAG) architectures. Deep expertise in API integration, preferably within the Azure ecosystem. Experience with Infrastructure as Code (IaC) across development, testing, and production environments. Solid understanding of Azure networking principles, security More ❯

libraries such as TensorFlow or PyTorch. Experience working with LLMs (Gemini), prompt engineering, and reinforcement learning from human feedback (RLHF). Experience with LangChain for building LLM applications with RAG pipelines and agent workflows. Practical understanding of vector search, embeddings, and retrieval-augmented generation (RAG). Experience building and deploying machine learning models into More ❯

architecture of LLMs. Foundational knowledge of diffusion models for image generation. Can display and present completed project/s using LLMs with a focus on any of the following: RAG, Agentic-RAG, fine-tuning Some experience or familiarity with deploying applications in the Cloud using services such as AWS or Azure. Proven track record in securing web/API applications. More ❯

varied use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term … cloud AI tools, observability platforms, and performance optimisation. This is an opportunity to work at the forefront of AI innovation, where your work will directly shape how next-generation systems interact, reason, and assist. More ❯

of AI engineers. Represent AI in senior product, engineering, and vendor forums. Generative AI Delivery Lead design, prototyping, and deployment of GenAI use cases (e.g. co-pilots, AI agents, RAG systems). Establish scalable LLMOps practices including model evaluation, governance, and lifecycle automation. Maintain awareness of emerging models and integration strategies. Machine Learning Engineering: Support the ML Engineer in model … Required Qualifications: Proven experience building and deploying GenAI applications in production. Strong hands-on knowledge of LLMs, prompt engineering, and retrieval-augmented generation (RAG). Practical experience with traditional ML, including data pipelines and MLOps workflows. Working knowledge of statistical modelling and experimentation. Proficiency in Python and at least one additional general-purpose language. More ❯

and US investors . Our founders have delivered cutting-edge AI at world-class research labs and high-growth technology companies. Now, operating in stealth, we apply next-generation agentic AI to overhaul mission-critical enterprise workflows that still depend on error-prone, manual processes. Our vision is to bring these high-value operations into the modern era … event buses (Kafka, Pulsar). Wrangle large, heterogeneous data sets —model, transform, and index multi-modal, multi-terabyte enterprise datasets for advanced AI workloads Develop enterprise-level next generation AI systems with the support of our AI specialists Ship complete customer features - from architecture and code to CI/CD, infra-as-code (Terraform), rollout, and user training. … contract. Thrive in an early-stage, high-ownership environment—prototype today, deploy tomorrow, iterate next week. Bonus Points Experience deploying or consuming LLM-powered services (OpenAI, open-source models, RAG, vector stores) can be a bonus. However, we consider many great candidates without previous AI experience. What we're offering: Base salary from £115,000 - £135,000. .. plus meaningful More ❯

integrated into enterprise applications to enhance user experience, decision-making, and automation. Exposure to modern AI application patterns such as: Retrieval-Augmented Generation (RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis More ❯

infrastructure Working knowledge of Kubernetes, security best practices, and cloud platforms (AWS, GCP, or Azure) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such as LangChain, LangSmith, or Chainlit More ❯

healthcare and cutting-edge LLM technology, shipping fast and solving meaningful problems every day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services … LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background in search, recommender systems, or ML infrastructure at scale Experience with RAG architectures, embeddings, and vector search Confidence working across GCP (or AWS/Azure) and infrastructure-as-code Familiarity with observability, performance tuning, and secure data practices A growth mindset, startup More ❯

working with Generative AI on a wide range of challenges in a fast-moving environment. Minimum Qualifications & Experience Strong understanding of the theory of generative AI systems e.g., LLMs, RAG, Graph networks Strong experience deploying LLM’s for searching pipelines Up to date with current LLM and NLP research Experience designing, developing and deploying production machine learning pipelines Strong background More ❯

for tangible business outcomes Deep, hands-on understanding of machine learning, agentic systems, and generative AI. Practical knowledge of the AI landscape: architectural trade-offs (e.g., fine-tuned vs. RAG), mitigating hallucination, and technology selection for specific use cases. Proven ability to define technical vision and strategy for new technology initiatives. Shape plans, create reusable architectural patterns and frameworks for More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and maintaining long-lived systems. Bonus Points for: Familiarity with financial services or private equity environments. Experience with Azure, Postgres, Streamlit, or similar tools. Practical experience with document-based RAG, Q&A, or fact extraction tasks. Thriving in small teams with fast feedback and iteration loops. Please, apply to learn more. More ❯

LLM-based UIs Awareness of AI governance , model auditing, and data privacy regulation (GDPR, DPA, etc.) Familiarity with reinforcement learning, retrieval-augmented generation (RAG), or dynamic reasoning workflows Offer Details Role Type: Contract (outside IR35) or Permanent Compensation: Market rates or six-figure salary Location: London EC4| Hybrid (some remote) ️ Start Date: August More ❯

to join their high-impact AI engineering team. This role is at the cutting edge of applied LLM development—rapidly prototyping intelligent systems that leverage autonomous agents, reasoning workflows, RAG pipelines, and real-time semantic search. You’ll work across frameworks like LangChain, LangGraph, CrewAI, and LangFlow, designing smart, multi-step agents that can interact with APIs, memory modules, structured … freedom to experiment and innovate. As a Generative & Agentic AI Engineer, you will: Build fast, iterative prototypes using LLMs and agent frameworks Design and implement agent workflows, memory handling, RAG systems, and semantic search Integrate vector DBs (Pinecone, FAISS, Chroma), cloud AI services, and APIs Develop lightweight UIs (Streamlit, Gradio, React) and observability for demos Work hands-on with prompt … and AI safety tools Requirements: Strong Python development skills Hands-on experinece working with GCP and Google teams Experience with LangChain, LangGraph, CrewAI, Autogen, or similar frameworks Background in RAG, embeddings, vector search, and memory architecture Familiarity with cloud AI platforms and deploying models via APIs Comfortable building end-to-end prototypes (backend to simple UI) Max 2-month notice More ❯

LLM-Augmented Software Generation and Transformation – Senior Researcher Slough (UK) Our purpose is to make the world more sustainable. Fujitsu’s Research and Development (R&D) is at the forefront of Fujitsu's sustainability transformation strategy, conducting cutting-edge research to tackle social challenges and improve our future world. Your role will involve Conducting research and … development in automated software engineering, including but not limited to code representation for LLM, automatic program repair, generative AI for test generation, next-generation user interface for AI coding agents, and architecture for AI coding agents. Providing technical leadership and collaborating with Fujitsu Global R&D teams to develop novel software development technologies in the generative … tier conferences and journals. A passion for software engineering with the ability to write clean code. Proven expertise in machine learning, deep learning and AI, particularly in LLM and RAG technologies, with knowledge of LLM Agents, explainable AI and/or graph ML as a plus. Proficiency in Python, PyTorch and/or Tensorflow, and popular data science libraries, as More ❯

and accessibility tools. Comprehensive knowledge of Semantic Web technologies (RDF/s, OWL), query languages (SPARQL) and validation/reasoning standards (SHACL, SPIN). Comprehensive knowledge of RAG and GraphRag systems and architecture. Experience building ontologies in the e-commerce and semantic search spaces. Knowledge Graph and RAG -AI Architecture. Desirable: Experience with OCR, Image captioning, object detection More ❯

Engineer The role is building AI based automation into back-office administration tasks. You'll be working with GenAI and Agentic AI, lots of AWS, NodeJS, Python, HuggingFace, LangChain, RAG techniques, interfacing with diverse data sets. The opportunity Work at the forefront of the industry. It's exciting, competitive, fast paced and challenging of course! You'll have the support More ❯

time using image embeddings, similarity search (e.g. CLIP, vector search), and traditional CV approaches (e.g. YOLO, MobileNet). LLM & RAG Systems: Design and implement pipelines that support retrieval-augmented generation, internal AI tools, and scalable content delivery. Experience with vector databases, agent frameworks, or data workflows is highly relevant. Deployment & MLOps: Own model deployment More ❯