276 to 300 of 400 Retrieval-Augmented Generation Jobs in the UK

issues, and have informed opinions on AI/ML system architecture AI‐serious: you've led or contributed to shipping LLM‐powered systems (agents, RAG, evaluation infrastructure) in production, and you have opinions on what works Comfortable with the player‐coach archetype: you don’t want to stop being technical ...

scalable, resilient, and production-grade systems Hands‐on experience developing AI‐powered applications, including large language models (LLMs), AI agents, retrieval‐augmented generation, prompt engineering, evaluation frameworks, and model optimization techniques Proven ability to take AI solutions from concept and experimentation through to production … power AI applications, intelligent automation solutions, and AI agents Develop and deploy advanced LLM‐powered applications using techniques such as retrieval‐augmented generation, prompt engineering, evaluation frameworks, and model optimization Create robust data and inference pipelines that support both real‐time and batch ...

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 ...

grade deployment. The role: - Design AI and GenAI architecture strategies and roadmaps - Build scalable AI/ML and Agentic AI platforms - Work across LLMs, RAG, embeddings and semantic search - Support AI deployment, governance and integration patterns - Collaborate with engineering, data, DevOps and business teams - Advise clients on AI platform capability … implementation strategy Tech environment: Python LLMs (prompt engineering, fine-tuning, RAG) LangChain/LangGraph Agent frameworks Vector databases MLOps/LLMOps AWS/Azure/GCP/Databricks Kubernetes & GPU infrastructure What you'll bring: - Experience designing enterprise AI or ML architectures - Strong grasp of cloud and modern data platforms ...

Robotics team following continued growth and investment. This is an exciting opportunity to join a multidisciplinary engineering team developing next-generation intelligent systems that combine AI, machine learning and robotics to solve complex real-world challenges. Working closely with software engineers, data scientists and product teams … modern software architecture. Experience working within Agile development environments. Excellent communication and stakeholder engagement skills. Desirable Experience with ROS/ROS2. Experience with LLMs, RAG or Agentic AI architectures. Knowledge of reinforcement learning or motion planning. Familiarity with Docker, Kubernetes and CI/CD. MSc or PhD in Computer Science ...

OAuth 2.0, OIDC, Entra ID, RBAC), zero-trust principles, and compliance in regulated financial services. Knowledge of AI-enabled or agentic solutions (retrieval-augmented generation, tool and API integration, human-in-the-loop) is a plus. Excellent communication and stakeholder influence skills, translating complex ...

Data Intelligence Platform. You will mentor colleagues and establish yourself as an AI thought leader. You will lead GenAI initiatives, work on RAG architectures, agentic systems, AI observability, and natural language querying of structured data, while collaborating with field engineering and solution architects. #J-18808-Ljbffr ...

design, build, and operate multi-agent AI systems on AWS. You'll own end-to-end implementations using Amazon Bedrock, MCP servers, and RAG pipelines, while ensuring observability, scalability, and secure integration with client applications. Hybrid and remote options offered, with offices in multiple cities. #J-18808-Ljbffr ...

design, build, and deploy Generative AI and AI-driven applications on Google Cloud Platform. The role focuses on delivered solutions for regulated insurance with RAG, vector stores, and integration of OpenAI and Claude APIs. You will work with stakeholders to create safe, scalable AI systems, aligning with governance and risk ...

pipeline experience AI Tooling: Hands‐on experience integrating LLMs, handling vector databases, and building robust chunking/embedding strategies Secure Architecture: Deploying LLM pipelines, RAG systems, and orchestration layers within secure, classification‐bound environments Local Implementations: Self-hosting, optimising, and containerising open‐source models (e.g., Llama, Mistral) on private ...

development best practices, ensuring long-term project success and high end-user adoption.### Skills Required* Python* Software Development* Structured Data* Unstructured Data* Vector Databases* RAG Architecture* Enterprise AI Solutions* AI System Architecture* Google Cloud Platform (GCP)* Technical Discovery Sessions* French (Fluent)* German (Fluent)* Multi-agent Systems* LangGraph* CrewAI* ADK* ReAct ...

tooling and in-engine features using engine test frameworks and headless runs, and wire results into CI and dashboards; Implement production LLM integrations (prompting, RAG/tool calls, guardrails/safety, observability, timeouts/retries, and cost controls); Collaborate with studio engineering, tech art, design, and QA to land integrations ...

experience with software development using Python or similar coding languages. Experience building pipelines for structured and unstructured data using both vector databases and RAG‐like architectures to power enterprise AI solutions. Experience taking production‐grade AI‐driven solutions from conception to launch, and architecting AI systems on cloud platforms (e.g. ...

teams – in forward deployed engineering, technical consulting, applied AI, or solutions architecture Production experience building AI applications including agentic systems, multi-agent orchestration, advanced RAG, evaluation frameworks, and deployment at scale in regulated enterprise environments A track record of building teams from early stage – hiring, developing, and retaining strong engineers ...

experience with software development using Python or similar coding languages. Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. Experience taking production-grade AI-driven solutions from conception to launch, and architecting AI systems on cloud platforms (e.g. ...

experience with software development using Python or similar coding languages. Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. Experience taking production-grade AI-driven solutions from conception to launch, and architecting AI systems on cloud platforms (e.g. ...

technical role - Solutions Engineering, Developer Relations, Founding Engineer, or similar. Hands-on AI/LLM product experience. You\'ve built with LLMs, you understand RAG, tool use, agents, and evals, and you know the practical limits of current models. Strong full-stack chops: TypeScript on the front end plus ...

facing technical role - Solutions Engineering, Developer Relations, Founding Engineer, or similar.Hands-on AI/LLM product experience. You've built with LLMs, you understand RAG, tool use, agents, and evals, and you know the practical limits of current models.Strong full-stack chops: TypeScript on the front end plus one strong ...

Delivery: Design, build, and deploy AI‐powered applications (chatbots, copilots, document intelligence, decision‐support tools) for insurance use cases; implement Retrieval‐Augmented Generation patterns; build LLM‐based workflows and agents. Cloud & Platform Engineering (GCP): Deploy solutions on GCP leveraging Vertex AI, BigQuery, Cloud … Generative AI & LLMs: Hands‐on experience with ChatGPT/OpenAI APIs and/or Anthropic Claude; prompt engineering, system prompts, evaluation techniques; implementation of RAG, vector databases, semantic search. Cloud Platforms: Strong experience with GCP, especially Vertex AI; Docker/containerised workloads; cloud security, IAM, cost management. Data & AI Tooling ...

scalable, reliable data products and AI services. The successful candidate will bring technical experience across generative AI, large language models, retrieval augmented generation, machine learning, computer vision, geospatial data science and MLOps. They will work with multidisciplinary teams to identify valuable use cases, shape … modern machine learning frameworks such as PyTorch, TensorFlow or Keras. Practical experience with generative AI, large language models, embedding models, retrieval augmented generation, AI agents, model evaluation and fine‐tuning techniques. Experience designing and operating end‐to‐end MLOps workflows, including model training, deployment ...

extend platform components—APIs, tooling, and bespoke logic. Architecture & Standards: Architect robust, scalable, and compliant Agentic AI solutions across industries. Lead multi‐agent orchestration, RAG pipelines, and LLM/SLM integration design. Champion observability, telemetry, and auditable AI standards. Build and maintain reusable frameworks and asset libraries. Team & Product: Help …/Vue, SQL, REST/GraphQL. DevOps proficiency: Docker, Kubernetes, CI/CD, and major cloud platforms. Expertise in LLMs, prompt engineering, vector DBs, RAG pipelines, and Agentic orchestration frameworks. Agentic/integrations: MCP, Salesforce/SAP/Other. Excellent communication with both technical and executive stakeholders. Mentorship capability ...

working with technologies including Azure Databricks, Azure Machine Learning, Azure AI Foundry, LangChain, LangGraph and Python, building enterprise AI applications using RAG, vector databases, prompt orchestration and tool-calling patterns. You'll need experience with: Deploying LLMs and machine learning models into Azure enterprise environments. Azure Databricks, Azure Machine Learning … ideally Azure AI Foundry. Building production AI applications using LangChain, LangGraph and Python. MLOps/LLMOps including deployment, monitoring, governance and lifecycle management. RAG, vector databases, prompt engineering and tool-calling patterns. Building end-to-end AI/ML pipelines from data preparation through to production. Working with technical ...

highly regulated environments Strong understanding of software architecture and API design IT WOULD BE NICE FOR THE AI SOFTWARE ENGINEER TO HAVE.... Experience with RAG pipelines and vector databases Knowledge of MLOps tooling and CI/CD pipelines Exposure to Palantir Foundry or similar data platforms Experience working on National … Engineer/Machine Learning Engineer/LLM Engineer/Python/AWS/Azure/Docker/Kubernetes/Generative AI/NLP/RAG/MLOps/UKSV DV/Outside IR35/London ...

highly regulated environments Strong understanding of software architecture and API design IT WOULD BE NICE FOR THE AI SOFTWARE ENGINEER TO HAVE. Experience with RAG pipelines and vector databases Knowledge of MLOps tooling and CI/CD pipelines Exposure to Palantir Foundry or similar data platforms Experience working on National … Engineer/Machine Learning Engineer/LLM Engineer/Python/AWS/Azure/Docker/Kubernetes/Generative AI/NLP/RAG/MLOps/UKSV DV/Outside IR35/London ...

surface it in usable, performant ways for brokers and analysts.* **AI-Enabled Development:** Build AI-enabled product features using patterns such as retrieval-augmented generation, tool and API integration, evaluation and human-in-the-loop design.* **AI-Assisted Engineering:** Use AI-assisted engineering tools ...