76 to 100 of 279 Retrieval-Augmented Generation Jobs in England

solutions using serverless, containerised and Kubernetes-based environments Generative AI and Large Language Model (LLM) deployment and hosting Retrieval Augmented Generation (RAG), vector databases, semantic search and embedding techniques Python development and AI frameworks such as PyTorch, TensorFlow and LangChain MLOps frameworks ...

production systems, including: Building and deploying LLM-based applications in production environments Designing agentic workflows and multi-step AI systems Implementing RAG (Retrieval-Augmented Generation) pipelines Working with OpenAI or Anthropic APIs Using vector databases and embedding-based search systems Applying prompt engineering techniques ...

GitHub Actions, Azure DevOps, RBAC and Cost Controls.* Software Engineering: Clean Code (SOLID, DRY), DDD, modular architecture, TDD, code quality tools. · AI Engineering (Desirable): RAG pipelines, agentic workflows, MCP, LLM integration (OpenAI, Gemini or other models from Huggingface), LangSmith, TruLens.* Agentic Frameworks (Desirable): LangChain, CrewAI, Autogen, OpenAI Agents, Google ADK.We ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

following: You are good at prototyping and experimenting with emerging technologies to assess feasibility and impact. You have knowledge of vector databases, RAG architectures and their integration into enterprise systems You have experience with GenAI development frameworks or orchestration tools such as Semantic Kernel, AutoGen, LangGraph, LangChain, or PromptFlow. ...

tooling) AWS/Azure cloud platforms (including AI services such as Bedrock) Data engineering tools (e.g. AWS Glue or equivalent) NLP, embeddings, vector databases, RAG architectures MLOps tooling for model deployment and monitoring Experience with distributed data systems and APIs Required Skills & Experience Several years' experience in software engineering, data ...

implementing end-to-end AI/ML solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure ...

journey The key things they are looking for are: Strong Python skills A good general understanding of LLMs Some exposure to GenAI, chatbots, NLP, RAG, prompt engineering or similar An interest in AI guardrails and responsible use of AI Good software engineering principles Awareness of security in modern development ...

enterprise‐scale AI and Generative AI platforms in production environments. Deep expertise in cloud‐based AI services, particularly Azure OpenAI, Cognitive Services, and advanced RAG architectures. Proven ability to design API‐first, microservices‐based AI systems integrated into complex enterprise ecosystems. Strong background in Azure infrastructure, networking, identity, and security ...

GitHub Actions, Azure DevOps, RBAC and Cost Controls. Software Engineering: Clean Code (SOLID, DRY), DDD, modular architecture, TDD, code quality tools. AI Engineering (Desirable): RAG pipelines, agentic workflows, MCP, LLM integration (OpenAI, Gemini or other models from Huggingface), LangSmith, TruLens. Agentic Frameworks (Desirable): LangChain, CrewAI, Autogen, OpenAI Agents, Google ADK. ...

defence turn complex data into actionable intelligence. You will be hands‐on in building Generative AI applications, deploying Retrieval‐Augmented Generation (RAG) architectures, and shaping the next generation of data‐driven tools within Movar's innovation ecosystem. Core Responsibilities Design and implement … cleaning to model deployment. Develop and fine‐tune Generative AI applications, including chat‐based tools, summarisation systems, and knowledge assistants. Build and manage RAG pipelines, integrating LLMs with enterprise data sources to deliver accurate, context‐aware outputs. Work closely with data engineers to ensure scalable, production‐ready deployments within Azure ...

Youll work at the intersection of: AI strategy Solution architecture Hands-on delivery What youll be doing: Architecture & Delivery Design and scale Generative AI, RAG, and Agentic systems in production Build enterprise AI platforms across AWS/Azure/GCP Select LLMs, vector databases, and orchestration frameworks Ensure solutions … youll need (must-have): Experience as an AI Architect/ML Architect/GenAI Architect Strong expertise in: Generative AI & LLMs, RAG (Retrieval-Augmented Generation), Agentic AI frameworks Hands-on with; LLMs (OpenAI, Llama, Claude, Mistral), Vector DBs (Pinecone, Weaviate, FAISS), Frameworks (LangChain, LlamaIndex ...

/ML and GenAI at enterprise scale — real impact, not just prototypes Expert Python with hands-on experience in PyTorch, TensorFlow, LangChain, LangGraph, RAG pipelines, and LLMs Solid MLOps/LLMOps fundamentals: CI/CD for ML, model governance, and evaluation frameworks Broad cloud experience across AWS, Azure ...

unstructured data and build reliable data and development pipelines. Vector Databases & Services - Hands‐on experience using vector databases and retrieval‐augmented generation (RAG) to store and retrieve data for LLMs. Skills Development for LLM Models - Understanding of selected LLM technologies and the ability ...

levels. What you'll bring: Proven experience (3 years) in AI solution engineering, with a focus on large language models, prompt engineering, and RAG implementations. Strong skills in data analytics, MLOps practices, and API development. Knowledge of governance frameworks for AI/ML models. Python expertise for automation. Experience working ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

datastores AI & Automation Hands-on experience integrating LLMs, AI APIs (OpenAI, Azure OpenAI, etc.), or ML models into production applications Familiarity with agentic frameworks, RAG pipelines, or AI-assisted workflow automation Ability to assess where AI genuinely adds value and communicate that clearly to non-technical stakeholders Mindset & Adaptability Proven ...

preferred) and modern MLOps practices. Commitment to people development: mentoring, performance conversations, and building high‐performing teams. Bonus: experience with NLP on financial contracts, RAG or LLM‐based applications on structured data, large‐scale loan data pipelines, or containerization tooling (Docker, Kubernetes, Terraform). Competitive salary and performance‐based bonus. ...

systems, software engineering, and cloud infrastructure. Key Responsibilities: Core Duties Design, implement, and maintain AI agent workflows, including retrieval‐augmented generation (RAG), orchestration, tool/function invocation, and policy‐based routing Build cloud‐native backend services and APIs to support AI‐driven applications … engineering experience with ownership of production systems 3+ years of hands‐on experience building and deploying AI/LLM‐based systems in production (agents, RAG pipelines, orchestration) Strong experience designing and delivering cloud‐native systems, including APIs, microservices, containers, and serverless or event‐driven architectures Proficiency in Python, Java ...

Integrate frontier and self-hosted LLMs (Claude, GPT, Gemini, open-weight models) with tools, data, and external systems through MCP and custom connectors. Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with Elasticsearch or Solr, and BM25 + similarity search. Work ...

Required Experience Strong programming skills, particularly in Python Experience with machine learning and AI, including LLMs or related technologies Knowledge of areas such as RAG architectures, agent-based AI, computer vision, or audio processing Experience working with cloud platforms (AWS or Azure preferred) Familiarity with Docker, Linux/Unix environments ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

Required Experience Strong programming skills, particularly in Python Experience with machine learning and AI, including LLMs or related technologies Knowledge of areas such as RAG architectures, agent-based AI, computer vision, or audio processing Experience working with cloud platforms (AWS or Azure preferred) Familiarity with Docker, Linux/Unix environments ...

including AI and ML services Solid foundation in software engineering principles for scalable, production-grade systems Experience designing and delivering enterprise AI solutions including RAG-based architectures using vector databases Proven experience delivering full-stack AI or ML systems within enterprise environments Strong understanding of advanced agent architectures, reasoning systems ...