176 to 200 of 279 Retrieval-Augmented Generation Jobs in England

half-defined. You move forward anyway. The stack they're using Python, FastAPI TypeScript, Next.js Azure MongoDB, Elasticsearch, Databricks, Neo4j LLMs, agentic workflows, RAG pipelines Syntax matters less to them than what you've shipped. An engineer well skilled with AI will be able to pick up new surface areas ...

platform capabilities in a B2B SaaS environment, with a track record of shipping at enterprise scale. Deep understanding of agentic AI patterns, LLM orchestration, RAG, fine-tuning, and evaluation frameworks — and the ability to translate these into compelling, reliable product experiences. Experience managing the full ML lifecycle — development, training, evaluation ...

multiple levels, from Engineers through to senior technical leadership. What you'll be doing Building AI-enabled platforms and intelligent workflows Working with LLMs, RAG pipelines and embedding models Developing scalable backend services and AI integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout … Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

shipping production-grade AI systems, not producing slide decks about them. ────────────────────────────── WHAT YOU'LL BE DOING ────────────────────────────── 🔧 Technical Architecture • Designing end-to-end LLM and RAG systems integrated into complex enterprise workflows • Deploying and optimising models on AWS Bedrock, Azure AI Foundry, and self-hosted GPU infrastructure (vLLM, SGLang, Ollama) • Building … engineering excellence ────────────────────────────── WHAT WE'RE LOOKING FOR ────────────────────────────── ✅ Expert Python — plus hands-on experience with LangChain, LangGraph, PyTorch, TensorFlow or equivalent ✅ Deep knowledge of LLMs, RAG, prompt engineering, fine-tuning, vector databases, and generative architectures ✅ Production AI/ML delivery experience — real-world impact matters more than credentials ✅ Cloud-agnostic experience ...

problem-solving skills; capable of designing and validating algorithms Strong communication skills, stakeholder engagement, and technical reporting Experience with Generative AI Retrieval-Augmented Generation, and cloud deployment Familiarity with financial data structures and API integration (desirable) Required Attributes: Highly motivated, adaptable, and able ...

application Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques likeparameterefficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generationpipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage andretrieval of embeddings generated by LLMs. rafting ...

enterprise-wide AI platform adoption Essential Skills and Experience Strong Python development experience, particularly for automation and test frameworks Experience with LLM and RAG evaluation tooling, frameworks, or custom evaluation pipelines Expertise in automated testing across unit, integration, and regression testing environments Good understanding of agentic AI systems, associated risks ...

leading a team of engineers, defining technical roadmaps, and maintaining high engineering standards. Ideal candidates should have extensive experience with AI technologies, especially in RAG and LLMs, as well as strong programming skills in Python, React, and TypeScript. The position is hybrid, requiring three days in the office weekly, located ...

hands-on software engineering background with experience writing production code Proven experience managing and growing engineering teams Strong understanding of AI concepts including LLMs, RAG, and AI-enabled architectures Genuine interest in AI technologies and modern software development practices Experience delivering high-quality SaaS or digital products in agile environments ...

things Comfortable working in a more entrepreneurial, less structured environment Strong academic background preferred Useful experience may include: FastAPI, React, TypeScript, Streamlit, APIs LLMs, RAG, agentic workflows, automation tools Internal tools, dashboards, workflow systems, CRM-type platforms AI-enabled process improvement or business automation This is a particularly strong opportunity ...

leading and developing engineering teams, including growing high-performing teams and improving delivery effectiveness. Strong understanding of modern AI concepts and architectures, including LLMs, RAG, and fine-tuning, with the ability to assess AI-driven technical proposals independently. Genuine interest in AI and emerging technologies, with hands-on experimentation using ...

prioritised backlog of agentic automation opportunities What You'll Need Solid understanding of how LLM-powered agentic systems work in practice — tool calling, RAG, memory, orchestration, prompt design, guardrails Background in Product Management, Business Analysis or a similar role where you've shipped AI or automation products Experience specifying ...

milestones.* Strong stakeholder leadership, with the credibility and communication skills to operate at C-suite level.## Desirable* Working knowledge of agentic AI, including LLMs, RAG and MCP architectures, sufficient to set direction and challenge a technical delivery team.* Experience with voice AI platforms such as ElevenLabs, Vapi, Retell or equivalent. ...

ready for production. You will combine strong engineering fundamentals (data modelling, integration, analytics) with practical Generative AI delivery (for example, retrieval‐augmented generation), and you will communicate clearly with both technical and non‐technical stakeholders. Key Responsibilities Lead discovery and solution shaping: run workshops … integrate where needed. Demonstrable skills delivering Generative AI solutions on Azure (for example Azure OpenAI/Azure AI services) including retrieval‐augmented generation, Azure AI Search, evaluation/quality measurement, and responsible AI considerations. Strong consulting skills: discovery, requirements, backlog shaping, and producing clear ...

features, from data ingestion and training to deployment, MLOps workflows, CI/CD, and model versioning Develop LLM/GenAI solutions: prompt engineering, RAG pipelines, embeddings, vector search, and inference optimisation (LoRA/PEFT, quantisation, GPU/TPU) Own observability across data, models, and prompts; run A/B tests … evaluation, and embed Responsible AI practices throughout What you'll need Hands-on LLM/GenAI experience (Gemini or open source) including fine-tuning, RAG pipelines, prompt engineering, and graph-based workflows (ADK, MCP) Strong Python, API/microservices development, GCP (Vertex AI, BigQuery), CI/CD, and containerisation (Docker ...

doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy … Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment ...

doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy … Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation → prototyping → production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

evidence extraction while building a product with immediate clinical and pharmaceutical impact. What you will do Architect production‐grade retrieval‐augmented generation and reasoning systems that handle heterogeneous data types across clinical and mechanistic domains. Design and implement sophisticated knowledge representation systems and ontology ...

play applied AI company — not a SaaS product with AI bolted on. We use fine-tuned open-source models, tool use, and retrieval-augmented generation. Users send 53% of the email drafts we generate. Since launching in March 2024, we’ve grown from $0 to $30M+ ...

enterprise Agentic AI platforms. You will move beyond traditional software QA to build continuous safety pipelines, ensuring our non-deterministic LLM agents, RAG systems, and tool integrations are secure, resilient, and compliant before production release click apply for full job details ...

now. We are hiring a Principal GenAI Engineer to define the architecture and technical direction behind our AI platform. Focus areas: - LLMs - Agentic AI - RAG - LLMOps • - Multi-agent systems - AI Infrastructure This is a true high-impact role with direct exposure to exec leadership and ownership over: - GenAI architecture - vendor ...

Looking For Essential: Strong background in software engineering, architecture or technical delivery Clear pivot into AI, with hands-on understanding of LLMs, agent architectures, RAG and modern AI ecosystems Experience leading client AI or digital transformation engagements Consultancy or professional services background Ability to translate AI capability into commercial value ...