226 to 250 of 277 Retrieval-Augmented Generation Jobs in the UK

The role The role is building AI applications based on LLM and models such as GPT and BERT You'll make use of Python programming, Pyspark, tensorflow, HuggingFace, LangChain, RAG techniques, interfacing with diverse data sets. Cloud data platforms and a diverse set of tools for AI app deployment. The opportunity Work at the forefront of the industry. It's … on experience in a commercial environment, working on AI/ML applications Multi cloud exposure (Azure/AWS/GCP) . Some of the following - Pytorch, GPT/BERT, RAG, Apache Airflow, Power Automate, Azure logic apps, RPA/Zapier, HuggingFace, LangChain... Background in Data Science or Software Engineering The values and ethos of this business Innovation with real purpose 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 ❯

their product, data, and software teams to build adaptive solutions that enhance productivity, automate workflows, and enable smarter business processes. This is an opportunity to shape how next-generation AI is applied in a fast-moving SME environment—balancing hands-on technical work with strategic innovation. Key Responsibilities Design, build, and deploy AI agents using frameworks such as … tools. Familiarity with APIs, databases, and cloud infrastructure (AWS, Azure, or GCP). Desirable: Experience in small, agile teams or start-up/SME environments. Knowledge of vector databases, RAG systems, or AI orchestration platforms. Interest in autonomous systems, cognitive architectures, or workflow automation. More ❯

Python preferred; additional exposure to JavaScript/TypeScript or Go beneficial). Strong background in AI systems, cloud-native architectures, and modern ML tooling (LangChain, OpenAI API, vector stores, RAG pipelines). Experience designing distributed, high-availability platforms deployed on AWS or similar cloud environments. Excellent communication and stakeholder management skills, with the ability to translate technical direction into business … to the property, SaaS, or workflow automation space is advantageous. Summary A rare opportunity for a technically strong, execution-focused engineering leader to shape and scale a next-generation AI automation platform from the ground up — with full ownership of the engineering strategy, team, and architecture. More ❯

build without frameworks. Confident in client-facing settings: workshops, presentations, advisory roles. Preferred: Advanced degree in AI, Computer Science, or related field. Experience with PyTorch/TensorFlow, vector databases, RAG, and orchestration tools. Background in start-ups (hands-on generalist) or consultancies (client-facing). Exposure to regulated industries. Independent, entrepreneurial mindset; comfortable working with autonomy. Why Join? Impact: Deliver … and contribute to the wider AI ecosystem. Culture: Join a fast-moving team operating at the frontier of applied AI. Agentic AI, Multi-Agent Systems, LLM Deployment, LangGraph, LangChain, RAG, AI Orchestration, AI Consultant, Software Engineering for AI, AI Productionisation, Autonomous AI Systems, AI Architect, AI Careers, AI Jobs Remote. 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

investors and is now entering a major build-and-scale phase. We’re looking for a hands-on Technical Lead to help architect, build and scale a next-generation investment intelligence platform. Role Overview You’ll be the technical spearhead for an ambitious product at the intersection of AI and finance. You’ll stay close to the code … workflows and automation. Solid understanding of SQL (PostgreSQL) and relational database design. Experience with event-driven systems (Pub/Sub, Kafka) and NoSQL databases (MongoDB). Nice to Have RAG, autonomous agents, Model Context Protocol (MCP) Blockchain/Web3 integrations or tokenisation frameworks Understanding of cryptography and distributed systems Background in fintech, wealth management or due-diligence automation What’s … On Offer Shape the future of how the world invests in alternative assets through AI High ownership role in shaping a next-generation AI fintech platform Close collaboration with an experienced CTO and founding team Opportunity to build and lead an elite engineering team Mission-driven culture with strong backing from top-tier investors Impactful work with exposure More ❯

operate as "Forward Deployed Engineers," bridging the gap between our clients' most ambitious goals and our core engineering teams. Our mission is to architect and deliver the next generation of commerce. We work on innovative projects from strategy through implementation, using the latest technologies – with a particular focus on Generative AI – to help our clients achieve market leadership. … client-facing engineering roles. Full Stack development expertise (Python, JavaScript). Hands-on cloud experience (Azure or GCP). Integration with Generative AI services and AI/ML patterns (RAG, MLOps). Strong database management skills. Nice-to-have: Cloud certifications (Azure/GCP). Experience with vector databases (Pinecone, Weaviate). Retail or CPG industry exposure. Why Accenture? Competitive … technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets, and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values include Stewardship, Best People, Client Value Creation, One Global Network More ❯

operate as "Forward Deployed Engineers," bridging the gap between our clients' most ambitious goals and our core engineering teams. Our mission is to architect and deliver the next generation of commerce. We work on innovative projects from strategy through implementation, using the latest technologies – with a particular focus on Generative AI – to help our clients achieve market leadership. … client-facing engineering roles. Full Stack development expertise (Python, JavaScript). Hands-on cloud experience (Azure or GCP). Integration with Generative AI services and AI/ML patterns (RAG, MLOps). Strong database management skills. Nice-to-have: Cloud certifications (Azure/GCP). Experience with vector databases (Pinecone, Weaviate). Retail or CPG industry exposure. Why Accenture? Competitive … technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets, and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values include Stewardship, Best People, Client Value Creation, One Global Network More ❯

AI Engineer | Remote – UK | Agentic | LLM | RAG | Applied AI | Outside IR35 | £500 - £600 pd | 3 Months Peaple Talent have partnered with a Tech Consultancy who are growing their AI practice and sourcing an AI Engineer/Machine Learning Engineer to work with an FS client on enterprise scale AI systems. You will ideally have a strong academic foundation which has … progressed into applied AI where you will be working with a plethora of technologies/projects including; RAG, LLM, MLOps MLFlow, Vertex AI and HuggingFace NLP with LangChain Python Agentic AI This is a client facing role where you will act as a consultant on AI transformation for the end client. This will be mostly remote but they are ideally … is likely to extend due to the pipeline of work being delivered and additional projects with other clients. Please apply for more information. AI Engineer | Remote – UK | Agentic | LLM | RAG | Applied AI | Outside IR35 | £500 - £600 pd | 3 Months More ❯

Software Engineer who can stand on their own two feet and deliver complex solutions with confidence. You’ll be joining a small, sharp team building AI-driven products — think RAG pipelines , GenAI integrations , and LLM-powered features that actually make an impact. This isn’t a role where you hide behind Jira tickets. You’ll be hands-on , trusted to … make big decisions, and involved across the full stack. You’ll be working with: 🧠 Python (Django) ⚡ JavaScript (React or Vue) ☁️ AWS 🔁 CI/CD 💾 SQL 🤖 GenAI, RAG pipelines & API integrations What we’re looking for: Someone who can deliver end-to-end technical solutions independently Excellent communicator (small team, no silos) Real-world experience with AI/GenAI projects A More ❯

Software Engineer who can stand on their own two feet and deliver complex solutions with confidence. You’ll be joining a small, sharp team building AI-driven products — think RAG pipelines , GenAI integrations , and LLM-powered features that actually make an impact. This isn’t a role where you hide behind Jira tickets. You’ll be hands-on , trusted to … make big decisions, and involved across the full stack. You’ll be working with: 🧠 Python (Django) ⚡ JavaScript (React or Vue) ☁️ AWS 🔁 CI/CD 💾 SQL 🤖 GenAI, RAG pipelines & API integrations What we’re looking for: Someone who can deliver end-to-end technical solutions independently Excellent communicator (small team, no silos) Real-world experience with AI/GenAI projects A More ❯

how data science drives commercial outcomes. Establish an AI Centre of Excellence to embed data-driven thinking across all functions. Deliver enterprise-scale GenAI solutions (eg copilots, virtual assistants, RAG systems) and lead classical ML initiatives. Define governance and ethical standards for responsible AI adoption. What We're Looking For Extensive senior leadership experience leading large, enterprise data science functions. … A strong track record of delivering production-grade AI/ML solutions and driving adoption at scale. Deep expertise in GenAI (LLMs, prompt engineering, RAG) as well as classical ML and experimentation. Hands-on knowledge of MLOps, cloud (AWS/Azure), and modern data science tools. Exceptional stakeholder skills - able to translate complex technical work into board-level strategy and More ❯

how data science drives commercial outcomes. Establish an AI Centre of Excellence to embed data-driven thinking across all functions. Deliver enterprise-scale GenAI solutions (e.g. copilots, virtual assistants, RAG systems) and lead classical ML initiatives. Define governance and ethical standards for responsible AI adoption. What We're Looking For Extensive senior leadership experience leading large, enterprise data science functions. … A strong track record of delivering production-grade AI/ML solutions and driving adoption at scale. Deep expertise in GenAI (LLMs, prompt engineering, RAG) as well as classical ML and experimentation. Hands-on knowledge of MLOps, cloud (AWS/Azure), and modern data science tools. Exceptional stakeholder skills - able to translate complex technical work into board-level strategy and 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 ❯

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 ❯

accuracy, and curriculum alignment Collaborate with product, engineering, and learning science teams to ship production-ready AI features Stay current with research in LLM alignment, quantization, and retrieval-augmented generation What we’re looking for: 4+ years experience in ML/AI engineering (LLMs, transformers, generative models) Strong Python skills and experience with PyTorch … Hugging Face, or similar frameworks Proven track record in latency optimization (e.g. batching, quantization, caching strategies) Familiarity with hallucination mitigation techniques (e.g. RAG, chain-of-thought, fact-checking) Bonus: experience in edtech, instructional design, or learning science More ❯

to prioritise AI use cases, structure governance, and integrate new capabilities into business models. Technical Credibility Deep understanding of modern AI ecosystems – including generative AI, agentic systems, AI copilots, RAG, orchestration tools (LangChain, AutoGen, CrewAI), and vector databases. Able to design AI-enabled solution architectures and guide technical teams. Combines classical data science literacy with applied experience in new-generation More ❯

environment. What You’ll Do Train and fine-tune LLMs and ML models using standardised experiments and high-quality data. Build and improve LLM-based systems for low-latency RAG systems, intent detection, call scoring, and email classification. Support real-time inference infrastructure with ultra-low latency and detailed monitoring. Create and maintain evaluation datasets to test safety, prompt following … and drive to improve model performance. Why Join Us Work on real applied LLMs from training to deployment. Collaborate with an ambitious, research-informed team. Shape the next generation of AI-driven communication tools. More ❯

Lead | £100,000 + 20% Bonus | Hybrid - 2 Days per Week in London 💰 £100,000 + 20% Bonus 📍 London - 2 days a week in office 🛠️ AWS, Python, Terraform, Kubernetes, RAG/GenAI interest Here at Burns Sheehan, we're exclusively partnered with a leading UK fintech that's transforming small business lending - processing billions in decisions annually. They've built … CloudFormation) and cloud-native development. Python scripting proficiency at platform engineer level but an understanding of software development lifecycle and production system ownership. Interest or experience in GenAI infrastructure - RAG, vector databases, LLM platforms, or agent frameworks. Strong communication skills and ability to translate technical decisions into business outcomes. This role is NOT an ML Engineering position - you won't More ❯