126 to 150 of 256 Retrieval-Augmented Generation Jobs in London

Financial Crime, customer onboarding, and real‐time decision management. Experience operationalising AI/ML solutions, including model lifecycle management, feature engineering, vector databases, RAG architectures, AI governance and production‐scale MLOps. Knowledge of payment, acquiring, merchant services, consumer lending, regulatory compliance, model risk management and responsible AI within highly regulated ...

cloud platform engineering who are looking to deepen their AI expertise. Understanding and hands‐on experience with LLM based applications and related technologies (e.g. RAG, vector databases, agent frameworks). Experience managing and deploying AI Agent and Knowledge Management systems is a plus (e.g Glean, Gemini Enterprise, Amazon Quick, Writer ...

time decision management Some other highly valued skills may include: Experience operationalising AI/ML solutions including model lifecycle management, feature engineering, vector databases, RAG architectures, AI governance and production‐scale MLOps Experience building modern data platforms incorporating data mesh/Lakehouse architectures, real‐time analytics, graph technologies, observability ...

know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external ...

prototypes, products and production ready solutions Designing and implementing end to end AI solutions that integrate with enterprise systems Working with LLMs, prompt engineering, RAG patterns, embeddings and fine tuning Developing AI agents and agentic workflows using modern frameworks Working with vector databases, APIs and modern data platforms Supporting … collaborative, high performing environment Experience Required: Strong Python and SQL experience Applied AI engineering, ML engineering or software engineering background Experience with LLMs, RAG, embeddings, prompt engineering or fine tuning Exposure to LangChain, LangGraph, Agent Development Kit or similar agent frameworks Experience with vector databases such as Pinecone, Chroma ...

development, building 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 … 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 ...

development, building 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 … Computer Science, Software Engineering, or a related field As a Software Engineer specializing in AI systems, you will help shape the next generation of intelligent applications by building scalable platforms that bring cutting-edge AI capabilities into real-world production environments - Design and build scalable backend platforms, APIs ...

Identifying opportunities to adopt AI into new or existing digital servicesLeading AI solution design using modern patterns such as retrieval‐augmented generation (RAG), multi-agent orchestration, vector search, prompt chaining, and model fine‐tuning.Demonstrating hands-on ability to assess technical feasibility of AI concepts … services, DevOps and waterfall environments before and know the challenges to bring the best of old and new worldAbility to develop our next generation of digital architects through formal line management and informal mentoring, coaching and learning developmentWHAT YOU'LL LOVE ABOUT WORKING HEREOur Digital Architects ...

egress controls). Strong understanding of AI/LLM capabilities and risks across the lifecycle (model onboarding/ingestion, retrieval/RAG patterns, model serving) and associated control points (traceability, access, data handling), including assessing control design and operational effectiveness in fast‐changing environments. Experience with agentic ...

Anthropic APIs. The role requires 3-5 years of experience, strong Python/TypeScript skills, and hands-on experience with LLMs, vector databases, and RAG pipelines. #J-18808-Ljbffr ...

multi-agent orchestration (beyond simple chatbots) Hands-on experience with LLM guardrails , cost optimization , and model evaluation frameworks Deep understanding and implementation experience in: RAG architectures Vector stores Embeddings Prompt and response evaluation Strong engineering hygiene with experience in:Automated testingCI/CD pipelinesObservability & monitoringCorrelation IDs and tracingContainerization with Docker ...

scale backend architecture Event-driven systems Developer platforms Production AI infrastructure Experience with technologies such as OpenAI, Anthropic, LangGraph, LangChain, Kubernetes, Kafka, Vector Databases, RAG, MCP, Temporal or similar would be highly advantageous. Ideal backgrounds Current or previous: Chief Technology Officer Founding CTO Technical Co-Founder VP Engineering Head ...

Experience building or deploying AI/LLM systems in production. Founder or founding engineer experience. Familiarity with AI agent tools: eval frameworks, agent tooling, RAG pipelines, and prompt engineering. Prior experience with React, TypeScript, and/or Go. Experience interfacing with customers or leading technical projects with external stakeholders. What ...

know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external ...

Cloud Platform) and CI/CD practices for AI workflows. Practical familiarity with vector databases, hybrid search techniques, and advanced chunking strategies for optimizing RAG systems. Knowledge of or previous experience within the travel sector or fast-paced B2C e-commerce environments. Familiarity with version control tools (Git) and working ...

modern data stacks, and can reason about system design. Practical experience building applications on LLMs (OpenAI, Anthropic, open-source), including prompt engineering and RAG architectures. Experience training, deploying or productionising ML models, and measuring whether they actually work. Ability to translate a vague business problem into a technical solution ...

systems, core banking platforms and the integration challenges of deploying AI within regulated market systems.* Strong command of modern AI capabilities: LLMs, agentic frameworks, RAG, ML inference, and MLOps, sufficient to lead technical teams and challenge engineering decisions.* Demonstrated ability to drive AI adoption at scale, navigating organisational complexity, change ...

inform modelling and business decisions. Build, tune, andoptimisemachine learning, deep learning, and generative AI models,leveragingboth established andcutting-edgetechniques. Designand buildLLM-powered systems— RAG pipelines, prompt and context engineering, fine-tuning, structured outputs, function calling, context-window management,and secure model integration;selectingappropriately between standard and reasoning (test-time-compute … Kubernetes), event-driven or microservices architectures, and distributed data processing at scale. Experience with embedding pipelines, vector search, and semantic retrieval for RAG systems. Knowledge of LLM evaluation techniques including structured evals frameworks, hallucination mitigation, benchmarking, and human-in-the-loop evaluation. Understanding of AI safety, responsible ...

have delivered AI solutions within it. Typical projects include: Building AI agents and autonomous workflows Developing LLM-powered applications and copilots Designing enterprise RAG and knowledge retrieval platforms Production deployment of Generative AI solutions AI integration into enterprise products and business platforms Scaling AI systems across cloud environments … ideally have experience with: Python OpenAI, Anthropic, Gemini, Llama or similar LLMs LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen or equivalent frameworks RAG, vector databases and embeddings Azure AI, AWS AI or Google Vertex AI MLOps, CI/CD and cloud-native deployment Docker, Kubernetes and modern software engineering practices What ...

covers the full AI/ML engineering lifecycle, from discovery to deployment and monitoring. Responsibilities include Designing and implementing agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP‐first stack. Building and maintaining production ML pipelines and services for non‐GenAI use cases (e.g. recommender … Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying and operating ML systems in production (batch and real‐time). Familiarity with RAG architectures, prompt engineering, guardrails and evaluation techniques. Previous experience developing agentic capabilities, such as agent skills, MCP servers, tool usage. Strong grasp of security, privacy ...

agent orchestration, tool use, skills use, and memory systems — and work hands‐on with foundation models through fine‐tuning, retrieval‐augmented generation (RAG), and custom model integration. A part of your work will also involve engineering the AI context layer that makes these systems … faithfulness, translating findings into design improvements Architect and implement foundation model integrations — selecting the right models, invocation patterns, and customization approaches (fine‐tuning, RAG, custom integration) based on capability, cost, and performance trade‐offs Design and build model adaptation and fine‐tuning pipelines, applying working knowledge of transformer‐based architectures ...

Studio. Experience designing and building LLM agents or AI-powered workflows using Azure OpenAI or similar technologies Knowledge of Retrieval Augmented Generation (RAG), vector search or agent orchestration frameworks would be advantageous If this sounds like a good fit, apply now as interviews commence ...

Linux-based environments and proficient in managing containerized workloads and automated CI/CD pipelines. Advanced RAG: Experience building production-ready Retrieval-Augmented Generation systems, including vector database management and semantic search optimization. Preferred Qualifications Experience in the insurance or financial services sector. Deep ...

mapping tools. Familiarity with Microsoft 365 cloud ecosystem, Azure integrations, document management systems and virtual data rooms. Exposure to generative AI integration, prompt engineering, RAG architecture and LLM orchestration frameworks. Understanding of security architecture principles and legal‐sector information governance. Legal & Commercial Skills Proven experience in a law firm, ALSP ...

work is genuinely varied: one week you’re designing a data API, the next you’re improving retrieval quality in a RAG pipeline, the next you’re working with infrastructure to get a service into production. We’re looking for someone who finds that energising rather than uncomfortable. ...