51 to 65 of 65 Retrieval-Augmented Generation Jobs in Slough

feedback loops. 25% Architect & scale Own reliability, latency, and cost. Design online/offline eval harnesses, canaries, and SLAs; operate GPUs/accelerators where needed. Stand up and harden RAG pipelines (indexing, retrieval policies, grounding, guardrails) and agent frameworks. Take basic infra ownership on GCP (or AWS/Azure): networking, autoscaling, CI/CD, IaC, observability, and cost … building production ML/back‐end systems; 2+ years leading while coding. Expert Python ; strong back‐end chops (e.g., FastAPI, gRPC, Postgres, pub/sub/streams). Agents & RAG: Fluency with at least one agent framework ( ADK preferred ). Proven track record shipping AI agents and building RAG pipelines. LLM + DS depth: Prompting/tooling, retrieval More ❯

Technical Leadership Define and lead the AI & Data Science vision and roadmap , aligned with business priorities. Provide technical oversight for AI initiatives across domains: Generative AI & LLMs (fine-tuning, RAG pipelines, multi-agent systems). Predictive Analytics & Time-Series Modeling . Computer Vision & Multimodal AI . Reinforcement Learning & Optimization . Knowledge Engineering & Semantic Search . Edge AI & Real-Time AI … and client engagements . Required Qualifications Significant experience in AI/ML, including experience in a technical leadership or team lead role . Strong knowledge (architectural & practical) of: LLMs, RAG, and AI Agents . Predictive analytics & time-series forecasting . Computer vision, multimodal learning, and geospatial AI . Reinforcement learning and optimization techniques . MLOps practices & data pipelines . Ability More ❯

ll Do Architecture & Development : Design and implement the cognitive systems that give AI agents consistent personalities, memory, and reasoning capabilities, using advanced LLM techniques like chain-of-thought prompting, RAG systems, and agentic tool use. Modeling & Experimentation : Design and run systematic experiments to evaluate agent behavior, test hypotheses about behavioral patterns, and iterate on model architectures based on empirical results … principles Experience working in fast-paced environments where requirements evolve rapidly Technical Skills LLM & Agent Development : Hands-on experience building applications with large language models, implementing advanced prompting techniques, RAG systems, and agentic workflows Backend Engineering : Proficient in Python and backend frameworks (e.g. FastAPI, Django, Flask); understanding of distributed systems and scalable architectures AI/ML Frameworks : Experience with PyTorch More ❯

directly on several key product enhancements. You’ll work with Python/Django, Javascript and AWS. Our platform is also increasingly AI-powered, so familiarity with implementing AI solutions (RAG etc) is an advantage. You’ll be working on a project where everything is hosted in AWS and we have a lightweight automated deployment process. You’ll work alongside a …/or ReactJS. Working understanding of data capture and performance tracking, a willingness to contribute to design and UX decisions. AI familiarity with working on Gen AI projects, including RAG and API integration. Ability to work within project timelines, proactively communicate any delays and contribute to task re-prioritisation to keep things on track Experience in developing websites, web applications More ❯

extraction, sentiment analysis, and qualitative insights Define reporting standards: Set quality bars for what makes a great dashboard (agent performance, app store trends, CSAT drivers, topic evolution, etc.) Optimize RAG pipelines: Design retrieval strategies and grounding approaches for report generation to ensure factual, relevant outputs Customer-Facing Analytics & Enablement (30%) Deliver bespoke insights: Partner with key … for accuracy and relevance, ensure quality before launch Operations & Optimization (10%) Monitor report health: Track delivery, engagement, and quality metrics; debug when outputs degrade Performance tuning: Optimize report generation costs and latency; balance API usage with quality Document everything: Maintain clear documentation for prompts, templates, and best practices What Makes You a Great FitTechnical Foundation 5+ years in … building customer insights, product analytics, or data-driven reporting AI-native experience: Hands-on work building reports or products using LLMs—prompt engineering, structured output generation, embeddings, RAG, summarization pipelines Python proficiency: Comfortable with pandas, OpenAI library, API integrations, and data manipulation for prototyping and analysis SQL fluency: Can write complex queries and understand data modeling Analytics tools More ❯

doing: • Owning the design and build of LLM-based systems end-to-end • Fine-tuning and adapting models (LoRA, instruction tuning, PEFT etc) rather than just prompt-engineering • Building RAG workflows, embedding strategies, memory layers and domain grounding • Working out how to measure output quality, reduce hallucination risk and improve robustness • Optimising inference performance (quantisation, distillation, pruning, batching, caching) • Deploying … prototyping/notebooks • Strong Python and experience with PyTorch/Hugging Face/similar tooling • Experience deploying models into production (not just training them) • Familiarity with vector stores and RAG patterns • Comfortable operating in a hybrid environment and speaking with stakeholders where required • Someone who enjoys solving real problems end-to-end: from understanding the domain → designing the approach → shipping More ❯

Are you an AI engineer with strong software engineering fundamentals? Have you deployed AI models into production—not just trained them? Want to work on agentic systems, RAG pipelines, and LLM-powered tools? We're working with a high-growth GenAI consultancy building real-world, production-grade AI applications. With a team of 50+, the company partners with global clients … deployment, collaborating with cross-functional teams and mentoring other engineers. Key Responsibilities Build and deploy scalable AI tools using Python and LLM APIs Design end-to-end agentic and RAG-based solutions for enterprise use Own delivery from proof-of-concept through to production Work with cloud-native architectures (e.g. AWS Lambda, Step Functions) Lead and guide junior engineers within More ❯

Do you want to build the infrastructure powering the next generation of AI agents? Have you scaled backend systems that drive automation at speed? Ready to join a profitable AI startup transforming how industries deploy technology? A profitable, fast-growing AI company is building the “roads” for AI agents — providing the infrastructure that lets businesses deploy and integrate …/LLM components into production systems. Role Breakdown: 50% Backend Engineering: FastAPI, Flask, Node.js, CI/CD 30% Data Engineering: ETL, DBT, Airflow 20% AI/LLM Integration: LangChain, RAG pipelines, orchestration Key Responsibilities: Design and build backend services to support AI agent deployment Develop scalable data pipelines and integration layers Implement AI/LLM-powered features with LangChain and … of Front-End, and adoption engineers Reporting Line: CTO Visa: Cannot sponsor Ideal Profile: 5–8 years’ experience in backend or data engineering (Python) Exposure to AI/LLMs, RAG, or personal AI side projects Experience in startups or scale-ups where you’ve helped build or grow systems Solid education and strong coding fundamentals Comfortable working in a small More ❯

Engineer , you’ll take ownership of core backend systems powering an advanced AI platform that integrates production-ready LLMs and agentic systems. You’ll architect scalable backend services, build RAG pipelines, and integrate AI components into distributed systems that enable real-world, production-scale AI deployment . 🔧 What You’ll Do Architect and develop backend systems (Python, FastAPI, Flask, NodeJS … Design and implement RAG pipelines (LangChain, Qdrant, embeddings) Build ETL & CI/CD workflows (Airflow, dbt, MLFlow) Integrate AI/LLM components into backend services Ensure reliability, scalability, and maintainability across systems Collaborate with a small, elite team of engineers and AI researchers 💼 About You 4+ years of backend experience (Python, distributed systems, async APIs) Proven experience integrating AI or More ❯

Do 🔧 Design and implement end-to-end data pipelines linking core platforms (CRM → data lake → AI systems) Architect schemas and transformation layers optimized for analytics, automation, and AI workflows (RAG/internal copilots) Build Python and API integrations , using low-code/no-code tools (Zapier, Make, Matillion) where it speeds things up Prototype internal automations and AI-driven tools … than waiting for perfect architecture ✨ Nice-to-Haves: Experience with CRM platforms (Zoho, HubSpot, Salesforce) and scripting Familiarity with Snowflake or modern cloud data warehouses Awareness of LLM APIs, RAG concepts, or AI workflow orchestration Who You Are 🎯 Early-career to mid-career (2–6 years experience), eager for ownership and growth A pragmatic builder who enjoys wiring systems together … data flow end-to-end Hands-on, execution-focused, and comfortable shipping fast rather than waiting for perfect architecture Curious about AI and automation, with experience or interest in RAG, internal copilots, or AI workflows Able to communicate clearly with technical and non-technical stakeholders 📩 Interested? If you’re a Python + data + integration engineer who enjoys building AI More ❯

and applied AI , taking ownership of projects end-to-end, from experimentation to deployment. If you What You’ll Do Design, implement, and ship production-grade LLM applications (prompting, RAG, evaluation, deployment). Develop and refine AI pipelines and retrieval systems using tools like LangChain, LangGraph, or custom frameworks. Own model evaluation frameworks to ensure quality, factuality, and …/ML applications. Proven track record of shipping AI products into production (not just research or prototypes). Preferred tech stack - TypeScript, React, LangChain, and LLM orchestration. Experience with RAG systems , vector databases (e.g. Pinecone, PGVector, Weaviate), and model evaluation . Comfortable working in a fast-paced startup environment with a high degree of autonomy. Exposure to agentic systems, prompt More ❯

user-focused AI services across our business. What You’ll Do Design and implement a comprehensive AI testing and evaluation framework for all AI solutions, including LLM-based tools, RAG systems, and third-party platforms. Define and document quality standards for semantic accuracy, factual consistency, bias, tone, and relevance. Develop reusable testing templates, data sets, and evaluation methods that can … LLM behaviour (accuracy, hallucination, bias, tone, etc.). Familiarity with tools like Trulens, HumanLoop, PromptLayer, or similar; experience designing QA approaches for GenAI environments. Knowledge of modern AI architectures (RAG pipelines, embeddings, API integrations such as OpenAI, Azure OpenAI, Anthropic). Experience designing and implementing structured test regimes in fast-evolving contexts. Excellent communication and facilitation skills, engaging both technical More ❯

🚀 AI/Data Engineer (AI) 📍 Location: London (Hybrid – 1 every other week) 💼 up to £600 a day (Inside IR35) The Role As a Data Engineer , you’ll join our cross-functional Product team of software engineers, ML specialists, domain experts More ❯

LLM tooling and libraries (e.g., Hugging Face Transformers/PEFT, tokenisers, spaCy or similar) Experience shipping NLP systems: prompt engineering, fine-tuning (e.g., LoRA/PEFT), vector search, and RAG-based services Great knowledge of NLP algorithms: tokenisation, embeddings, attention, language modelling, text classification/generation, and information retrieval Desirable Skills 👌 Can speak, or learning to More ❯

translate business needs into scalable architecture on the Microsoft platform. The ideal candidate will be a thought leader, trusted advisor, and hands-on architect capable of shaping next-generation data platforms and driving adoption across stakeholder groups. Lead end-to-end architecture design and implementation for Microsoft Fabric across enterprise data programs. Collaborate with business and technical stakeholders … Lakehouse architecture, Delta/Parquet formats, and data governance tools. Experience integrating Fabric with Azure-native services (e.g., Azure AI Foundry, Purview, Entra ID). Familiarity with Generative AI, RAG-based architecture, and Fabric Notebooks is a plus. About Capgemini Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and More ❯