176 to 200 of 253 Retrieval-Augmented Generation Jobs in London

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 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 ...

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. ...

systems on cloud platforms (e.g., Google Cloud Platform (GCP)). Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. Experience leading technical discovery sessions. Preferred qualifications: Master s or PhD in AI, Computer Science, or a related technical ...

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. ...

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 ...

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 ...

experience writing and shipping Python in a professional engineering environment Experience designing, building, or deploying large language model applications, including prompting, evaluation, retrieval-augmented generation, and agentic orchestration Familiarity with the modern generative AI stack, including large language model APIs, vector stores, embeddings, tool ...

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 ...

horizon memory and knowledge infrastructure that will allow agents to learn, recall, and reason over vast, evolving enterprise contexts. This is not about simple RAG; it’s about pioneering the future of agent memory (episodic, semantic, and procedural). Innovate in Search & Retrieval: Develop state ...

extract patterns from one-off work and turn them into reusable assets for others.**Applied AI and Agentic Systems*** Experience building with LLMs, agents, RAG, tool use, workflow orchestration, or automation systems.* Ability to reason about multi-agent, human-in-the-loop, and enterprise integration patterns.* Comfort debugging agent behavior ...

Location: UK - Hybrid/Remote with client travel Salary: £90,000 - £110,000 + bonus and benefits Skills: Enterprise AI Transformation, Generative AI, LLMs, RAG, AI Agents, Workflow Automation, AI Prototyping, Technology Consulting, Discovery Workshops, Pre-Sales, Proposition Development The opportunity We are working with a growing technology and transformation ...

governance across multiple accounts, while communicating effectively with business stakeholders. The role requires hands-on Azure Foundry and OpenAI experience, strong modeling and RAG-based knowledge, and the ability to oversee secure, scalable AI workloads in regulated environments. #J-18808-Ljbffr ...

Senior AI Evangelist to lead design and delivery of Generative AI solutions for clients. You will architect end-to-end AI systems, including LLMs, RAG, multi-agent workflows, and MCP-based integrations, collaborating with cross‐functional teams to meet defined metrics. The role requires hands-on expertise with modern ...

Technology, Media or Telecoms sectors. Deep understanding of the Generative AI landscape and its commercial applications, with familiarity across technologies such as LLMs, RAG architectures, vector search, prompt design and AI platform operations. Experience overseeing the end-to-end delivery of AI initiatives, working closely with technical specialists to shape ...

Significant engineering experience with cloud-native systems (APIs, microservices, containerization, serverless) Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments Considerable experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models, including building abstraction layers to manage multi-provider pipelines ...

ingestion, storage, governance, analytics, model deployment, monitoring, and integration with enterprise applications. Understanding of generative AI solution patterns, such as retrieval‐augmented generation, enterprise search, intelligent assistants, document processing, content generation, and AI‐enabled workflow automation. Awareness of AI governance, responsible … Model Garden, Document AI, Gemini, or other Google Cloud data, analytics, API management, AI, or generative AI services. Experience designing retrieval‐augmented generation architectures using vector search, embeddings, enterprise data sources, access controls, and secure application integration patterns. Experience with MLOps, including model lifecycle ...