26 to 50 of 65 Retrieval-Augmented Generation Jobs in Slough

with lightweight, framework‐agnostic patterns is valued. Data & infra: SQL, Redis, managed vector DB, S3‐compatible storage, Docket, Sentry. Patterns: function/tool calling, retrieval‐augmented generation (RAG), structured outputs, self‐checks, retries/backoffs, circuit breakers, streaming, KV/cache management. Performance: batching, token/latency/cost monitoring, provider fallbacks, model selection … outputs, context management. Comfortable designing APIs, writing tests, instrumenting systems, and debugging failures across model/provider boundaries. Nice to have Experience with LangChain/LangSmith, Orq.ai, LlamaIndex, DSPy, RAG, Guardrails/Outlines/Instructor. Vector DBs, embeddings, and data pipelines. First 90 days - example outcomes Review and improve existing core Agentic workflows Improve the quality of existing LLM outputs More ❯

build, ship, and take responsibility. Proven Builder – 2+ years as an engineer with a track record of fast, production-grade code shipping. AI Native – You’ve built with LLMs, RAG pipelines, or agentic systems. You understand their power, and their limits. You’re fluent in: 🐍 Python/FastAPI ⚛️ TypeScript/React/Next.js 🗄️ PostgreSQL, relational databases ☁️ Cloud infrastructure (AWS/ More ❯

decisions. Own operability: set up and maintain CI/CD, monitoring/alerting, performance & error budgets, and safe rollback paths. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Essential Requirements 3–6+ years building production mobile apps (React Native), with shipped apps. Strong React Native proficiency (Expo More ❯

decisions. Own operability: set up and maintain CI/CD, monitoring/alerting, performance & error budgets, and safe rollback paths. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Essential Requirements 2–4+ years building production backends/services , with shipped work and clear ownership. Strong Python (FastAPI More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They’re More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They’re More ❯

categories AI You will have: Actively used AI in your workflows Deployed AI systems at scale Built real-world systems with LLMs Used agent frameworks, like LangGraph or LangChain, RAG pipelines It would be interesting if you have: Contributed to the AI/ML community (open-source, publications, talks) Experienced optimising AI models through fine-tuning, distillation, or human-in More ❯

team that’s pushing boundaries in autonomous AI What You’ll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical solutions What You’ll More ❯

JavaScript, C#) Proven experience building solutions with the Microsoft Power Platform Familiarity with Azure or AWS A strong interest in AI/LLMs. Practical experience with Dify, or building RAG applications is a major plus. More ❯

Face Transformers, spaCy, NLTK) ● Hands-on experience with LLMs (GPT, Claude, Llama, Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) ● Proven track record designing scalable AI architectures (RAG, Graph RAG, multi-agent systems) ● Experience with vector databases (Pinecone, Weaviate, FAISS) and major cloud platforms (AWS, Azure, GCP) ● Strong foundation in MLOps/LLMOps and Agile delivery ● Excellent leadership More ❯

Strong academic background : BSc, MSc or PhD in Computer Science or related STEM subjects from leading UK universities 3+ years of professional experience Strong experience working with LLMs and RAG pipelines Machine Learning expertise Fluency in Python and modern ML/AI frameworks Comfortable wearing multiple hats Experience in a start-up or fast-paced environment You care deeply about More ❯

deploy AI/ML systems that boost accuracy, speed, and user experience. Partner with domain experts to turn complex workflows into AI-driven automation . Leverage LLMs, LangChain, and RAG at scale. Ship quickly, iterate fast, and set the foundation for our engineering culture . Requirements 2+ years in AI/ML engineering or applied research. Proven track record of More ❯

Vision/NLP Strong Software 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 a week, so we would More ❯

of AI/ML solutions. 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 emerging technologies. Experience & Skills …/professional services experience is a 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. More ❯

distributed systems and AI research. You'll have 2+ years of professional experience in big tech or start-up Proficiency in Python Strong hands on expertise with LLMs and RAG Traditional ML experience Based in London and in office by default Looking to throw everything into a business from the early days This is an exciting opportunity to join a More ❯

considerations in deploying AI systems. Desirable: Experience with agentic AI frameworks (e.g. AutoGPT, CrewAI, LangGraph, ReAct). Background in conversational design, human-AI interaction, or reinforcement learning. Familiarity with RAG, vector databases, or simulation environments. More ❯

Storage , Queues , and SQL/NoSQL databases to build scalable, event-driven systems. Design and optimise APIs that serve our mobile and web products, including partner integrations. Implement efficient RAG AI backend systems for introducing advanced LLM based features Collaborate with product managers, data engineers, and frontend developers to deliver new features end to end. Write clean, maintainable, and well More ❯

/day (negotiable) About the Role: We are seeking a Senior Gen AI Engineer to develop and deploy advanced Generative AI products, including fine-tuning LLMs and implementing Graph RAG solutions. You will implement and optimize Gen AI agents for the Global Supply Chain, delivering scalable, secure, and cost-effective AI solutions. Key Responsibilities: Prototype and deploy Generative AI applications … for business objectives. Implement LLMs, Graph RAG, and AI/ML solutions in production environments. Develop automated processes (LLMOps) and CI/CD pipelines. Collaborate with cross-functional teams to integrate AI into business workflows. Ensure security, compliance, and AI governance standards. Educate IT and business teams on Gen AI capabilities. Required Experience & Skills: Higher degree (PhD preferred) in CS … Statistics, Physics, Bioinformatics, or related field. 5+ years of hands-on experience across AI domains: NLP, NLG, CV, ML, GenAI. Expertise in fine-tuning/training LLMs and Graph RAG solutions in production. Proficiency in cloud services and tools: Azure, Databricks, Azure ML, Vertex AI, Azure AI Services, Azure DevOps. Strong Python and LLM libraries experience: langchain, LangGraph, semantic kernel More ❯

ML systems Deep knowledge of LLM architectures and attention mechanisms – experienced with MoE, MHA, SWA, Flash Attention, GPT, LLaMA, DeepSeek, and reasoning-based models Experience in creating and maintaining RAG supported systems including PDF extraction, OCR, and varying chunking and embedding methodologies Inference optimisation experience – hands-on with high-performance inference frameworks such as vLLM, TensorRT, and related acceleration techniques … Proven evaluation experience – successfully conducted assessments of RAG and agentic systems. Both on performance and compliance (harm, guardrails, etc) 4+ years of development competency across a variety of languages, frameworks, and tooling, such as Python, R, Kubernetes, Kubeflow, JavaScript/JVM, Kafka Background in Application development, deployment, and monitoring on Kubernetes Proficient in PyTorch or similar frameworks (e.g Tensorflow) – demonstrates More ❯

production-grade code shipping. Applied LLMs - Experience deploying multimodal foundation model-based products. Deep knowledge of LLM research, agents, tools, and function calling. Deploying LLMs - Experience with LangChain, indexing, RAG, semantic search, and fine-tuning (SFT/RL) of LLMs/agents. Why Join £100,000 – £170,000 base salary + meaningful equity Visa sponsorship and relocation support available A More ❯

LLMs, prompt engineering, and agentic AI Desirable: ☑️ Experience with frameworks such as AutoGPT, CrewAI, or LangGraph ☑️ Background in conversational UX or human-AI interaction ☑️ Knowledge of vector databases or RAG techniques ☑️ Awareness of ethical and safety considerations in AI system If you'd like to learn more, feel free to reach out directly or email for a confidential chat. Role More ❯

Lead AI Engineer/Consultant - Greenfield Outside IR35 £600 - £750pd flexible for the right person RAG experience a must. About the Company Market leading SaaS organisation building out their AI function. About the Role This project is a greenfield build out of AI capabilities. They are starting with RAG use cases as a low bar, but are targeting a rich … deliver an AI capability. Lead design, architecture, and delivery of advanced AI/ML and generative AI solutions, ensuring scalable, secure, and production-ready system. Expert in MCP and RAG patterns. Design and build robust data and ingestion pipelines, integrate vector databases, and RSG. Expert-level proficiency in Python and key ML libraries (Langchain, Semantic Kernel, PyTorch, TensorFlow). Hands … complex, data-rich environments. Skilled in setting team engineering practices: Git, CI/CD, automated testing for ML code, code reviews, and documentation. Preferred Skills Expert in MCP and RAG patterns . Experience in developing agentic AI systems Lead tech enablement and mentor engineers, fostering culture of reliability, continuous improvement, and collaboration. Excellent communication skills, able to translate technical strategy More ❯

function Comfort working in a fast-moving, ambiguous environment An eye for quality and a bias toward shipping Bonus: experience in building AI-powered tools or creative applications (e.g. RAG systems, assistants, agents, etc. More ❯

expertise and strategic thinking. What You’ll Do Design, build, and deploy AI-powered systems and features that enhance product functionality and user experience. Develop and integrate agentic frameworks, RAG pipelines, and vector database architectures to support natural language and intelligent automation use cases. Fine-tune and customise LLMs (e.g. OpenAI, Anthropic, and other models) for specific business applications. Collaborate … and scaling AI models in Azure and/or AWS environments. Skilled in using LangChain, LangGraph, vector databases, and prompt engineering. Understanding of LLM optimisation, including context extension and RAG-based frameworks. Proven ability to collaborate cross-functionally and communicate complex AI concepts to non-technical teams. A proactive, creative mindset — comfortable operating with autonomy to build scalable, production-grade … Why This Role Play a central role in defining and executing an organisation-wide AI roadmap. Work directly with senior leadership on strategy and implementation. Shape the next generation of intelligent, data-driven products. Hybrid working environment with flexibility for deep technical focus and collaboration. Competitive compensation and the opportunity to build at scale in a high-growth More ❯

researchers, engineers, and portfolio specialists, solving complex real-world problems. Competitive compensation with a performance-linked bonus. What You’ll Do Take ownership of designing and building next-generation AI systems that make complex financial data instantly accessible and actionable. Partner closely with data scientists, ML researchers, and frontend engineers to turn research concepts into robust, scalable production … deployment practices using Docker, Kubernetes, and CI/CD pipelines. Comfort working in cloud-based environments (AWS preferred), including data connectivity and infrastructure-as-code principles. Experience integrating LLMs, RAG pipelines, or vector databases into production workflows is a major plus. A strong communicator who can translate technical complexity into business value and thrives in collaborative, cross-functional environments. Passion More ❯