326 to 350 of 400 Retrieval-Augmented Generation Jobs in the UK

forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality. You are An AI Native … native systems (APIs, microservices, containerization, serverless). Minimum of 1 year of hands-on experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production or near-production environments. Experience with modern AI platforms — OpenAI, Claude, Vertex AI, or open-source models — including building or using abstraction ...

operations, and own what you ship. What You'll Do Build and deploy production AI applications using LLMs, including agent workflows, tool use and RAG pipelines over company data Contribute to evaluation frameworks covering accuracy, latency, cost and reliability, with the extra rigour a regulated industry demands Implement retrieval … whether in production, at work or through substantial personal projects, with a genuine understanding of their capabilities, limitations and failure modes Practical familiarity with RAG architectures, vector databases and prompt engineering Exposure to agent frameworks (LangGraph, Claude Agent SDK, OpenAI SDK) or equivalent custom implementations An interest in LLM evaluation ...

tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams. Applies knowledge … multiple modern programming languages including Python, Golang, TypeScript and similar technologies in an AWS/Cloud environment. Experience in the use of GPT, LLM, RAG, Gen AI, Prompt Engineering related technologies Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages ...

CloudWatch, Splunk, Docker/ECS). Optimize application performance and reliability. Build and integrate AI-driven features (LLMs, intelligent APIs, automation workflows). Implement RAG patterns, AI pipelines, and model integrations (Bedrock, SageMaker). Optimize and monitor models for accuracy, drift, bias, and observability. Leverage Claude/equivalent frontier models … back end app dev side Front end experience a plus but not necessarily needed Python/typescript for coding Understand AI capabilities, handle RAG patterns Benefits at IntelliBee Long-Term Stability: Join us on a multi-year opportunities with room to grow. Comprehensive Health Coverage: Access quality healthcare benefits ...

reference architectures for investment and front‐to‐back office use cases. Drive integration of AI capabilities with core data platforms and content platforms, leveraging RAG and MCP to unlock proprietary data assets. Governance & Risk: Design and operationalize AI governance, covering model risk management, explainability, bias monitoring, data lineage, and regulatory …/ML architecture in large, complex enterprise environments. Hands‐on command of modern AI stack: LLM APIs and fine‐tuning, vector databases, RAG, embedding pipelines, prompt engineering, and agent orchestration frameworks (LangChain, AutoGen, or equivalents). Practical exposure to agentic AI architecture, multi‐agent coordination, and MCP or similar frameworks. ...

modelling, machine learning, predictive analytics, and decision science delivered into production environments and real business processes. Generative AI (Secondary): LLM‐powered applications such as RAG pipelines and document intelligence, applied where appropriate to support data‐led solutions. Autonomous AI (Emerging): Agent‐based systems and AI workflows, used selectively where they … lifecycle management. Proven ability to translate business problems into data science solutions that deliver measurable outcomes. Beneficial Exposure to Generative AI (e.g. LLMs, RAG) is beneficial but not a substitute for core data science expertise. Awareness of emerging AI paradigms (e.g. agent‐based systems) is a plus, but not required. ...

Experience in cybersecurity, email security, or adjacent enterprise SaaS domains preferred.* Familiarity with AI/ML concepts including prompt engineering, model evaluation, retrieval-augmented generation, and agent orchestration frameworks is valuable but can be developed in-role.* Prior experience in a developer enablement, developer ...

Requirements gathering from business users AI & Automation Experience with Generative AI tools Prompt engineering Basic understanding of LLMs and RAG (Retrieval Augmented Generation) AI use case identification, assessment and delivering from concept through to implementation and adoption. Familiarity with Microsoft Copilot, ChatGPT Enterprise ...

Forward Deployed Engineer programme. Key Responsibilities Architect and govern production‐grade agentic systems at enterprise scale: multi‐agent orchestration across complex environments, RAG pipelines, policy‐based routing, memory management, and programme‐level lifecycle observability Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure … depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOps fundamentals: eval harness design, prompt versioning, and production observability Cloud‐native engineering maturity: Kubernetes ...

closely with engineers to translate validated approaches into scalable, customer‐facing solutions. Key Responsibilities: Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies, patent search). Define agentic workflows and reasoning strategies for multi‐step IP tasks. Develop retrieval strategies, including hybrid search (semantic … Strong experience designing and running experiments, including model evaluation and iteration. Strong coding skills in Python. Experience with generative AI techniques (e.g., prompt engineering, RAG). Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases. Experience designing agentic workflows and reasoning strategies, with hands ...

closely with engineers to translate validated approaches into scalable, customer-facing solutions. **Key Responsibilities:*** Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies, patent search).* Define agentic workflows and reasoning strategies for multi-step IP tasks.* Develop retrieval strategies, including hybrid search (semantic … Strong experience designing and running experiments, including model evaluation and iteration.* Strong coding skills in Python.* Experience with generative AI techniques (e.g., prompt engineering, RAG).* Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases.* Experience designing agentic workflows and reasoning strategies, with hands ...

Manchester Hours Full time Working Pattern Hybrid, 40% (or two days) in our Manchester office. What you'll be doing Develop the next generation of autonomous AI for colleagues and customers, focusing on safety, security and scalability. Lead multiple engineering teams to design, build and run AI agents … enabling teams to deliver at pace. Hands‐on experience with agentic AI: building LLM‐powered applications/agents, tool use, orchestration, retrieval (RAG), prompt and safety engineering, evaluation and telemetry. Proficient knowledge of cloud‐native engineering: containers (GKE/Cloud Run), Terraform/IaC, CI/CD, automated ...

Claims domains. The role owns the patterns, frameworks, and standards for agent orchestration, MCP-based tool ecosystems, agent memory, planning, evaluation, and safety. RAG and conversational features are table stakes; the forward agenda is autonomous and semi-autonomous agents that act on behalf of users - quote intake, claims triage, underwriting … tool/function calling patterns reusable across product domains. Enable and provide support for domain teams for vertical insurance agents and the horizontal capabilities (RAG, retrieval, instructional flows) they compose from. Define and enforce levels of autonomy — assistive, semi-autonomous, autonomous — with explicit human-in-the-loop checkpoints ...

maintain robust automation solutions that streamline workflows and enhance operational efficiency. Collaborate with cross-functional teams to extend existing agent-based solutions and RAG implementations. Take ownership of the full software lifecycle: build, deploy, monitor, and tune solutions in a live environment. Ensure production maturity by transitioning solutions from proof … practises. Production Focus : A solid engineering mindset with the capability to extend existing automation rather than starting from scratch. Desired Qualifications: Experience with RAG architecture implementation and vector databases. Exposure to Azure AI and OpenAI technologies. Familiarity with enterprise workflow automation and agentic AI frameworks. Financial Services Adecco ...

maintain robust automation solutions that streamline workflows and enhance operational efficiency. Collaborate with cross-functional teams to extend existing agent-based solutions and RAG implementations. Take ownership of the full software lifecycle: build, deploy, monitor, and tune solutions in a live environment. Ensure production maturity by transitioning solutions from proof … practises. Production Focus : A solid engineering mindset with the capability to extend existing automation rather than starting from scratch. Desired Qualifications: Experience with RAG architecture implementation and vector databases. Exposure to Azure AI and OpenAI technologies. Familiarity with enterprise workflow automation and agentic AI frameworks. Financial Services Adecco ...

automated document classification, geospatial modeling of electric vehicle demand, demand forecasting, predictive maintenance, price optimization, and developing and maintaining production‐grade agentic RAG systems that serve the entire firm. What You’ll Do Data Management Perform data availability/quality assessments Develop comprehensive ETL processes Data Analysis Perform exploratory data … Analysis (Graph Theory), and Regressions Experience in AI/ML concepts: Cross Validation, Ensemble Methods, Over/Under Fitting, Accuracy/Precision/Recall, RAG, Agents Understanding of AI/ML performance measurement best practices including QA/QC framework, drift, bias, and fairness Experience in computer science concepts: Hashing ...

trends within the software industry Built or integrated AI/ML features into products and systems, including recommendation engines, NLP, computer vision, predictive analytics, RAG pipelines, and agents Successfully led modernisation and replatforming efforts to align legacy systems with short and long‐term business needs Led and delivered solutions … platforms, and infrastructure that other teams build on Data Engineering: including platforms like Databricks for data processing and analytics Delivering production agentic systems, including RAG pipelines, vector search, and agentic frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and n8n DevSecOps & DevOps MLOps Infrastructure as Code How You’ll Be Supported ...

logging, and safe rollout patterns Preferred Qualifications, Capabilities, and Skills Experience developing generative AI applications, AI agents, vector search, and retrieval‐augmented generation patterns Experience building AI agents using frameworks such as LangChain, CrewAI, LangGraph, or similar orchestration platforms Experience operating or integrating serving … platforms such as KServe, Ray Serve, NVIDIA Triton Inference Server, Text Generation Inference (TGI), alongside vLLM/llm‐d Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed model hosting Experience with online LLM quality monitoring (e.g., hallucination, toxicity, drift detection) and tracing via OpenTelemetry ...

aligned with engineering standards. Comfortable working across technical implementation, team enablement, stakeholder communication, and operational rollout. Curious about areas such as AI agents, MCP, RAG, evals, prompt/workflow design, AI observability, and developer productivity measurement. The Impact You Will Have Help engineering teams adopt AI in a way that … security, and release discipline. Experience with design systems, frontend platform standards, accessibility, and reusable UI patterns. Experience with knowledge retrieval, documentation systems, RAG‐style workflows, or internal engineering knowledge platforms. Experience designing lightweight evaluation approaches for AI‐generated outputs, automation, or agentic workflows. Experience coaching engineers, running workshops ...

PrivateLink for cross-cloud data connectivity. AI Readiness : Practical experience architecting data infrastructure for AI/ML consumption - vector databases, embedding stores, and RAG pipeline integration. Soft Skills: Strong interpersonal skills; ability to translate complex data architecture into clear language for Business Analysts and non-technical stakeholders. Duties Data Modelling … warehouse scheduling strategies to reduce operational data spend. AI Readiness: Architect data stores purpose-built for LLM consumption - including vector databases, embedding pipelines, and RAG-compatible data structures that will serve as the foundation for Bestway's AI product layer. Data Contracts : Partner with Business Analysts to formally define ...

Copilot deployments, including security, prompt design and adoption tracking Help design, prototype, and test AI solutions, including Large Language Model (LLM) integrations, retrieval-augmented generation, agentic workflows and structured-output patterns Build well-scoped components and ship them through GitHub and Azure DevOps pipelines ...

Skills for the Artificial Intelligence Architect: Proven experience architecting solutions across Generative AI, Agentic AI, machine learning and automation Strong understanding of prompt engineering, RAG pipelines, model tuning, MLOps and LLMOps Experience delivering enterprise‐scale RAG solutions using LLMs and vector databases Ability to design AI platforms that integrate with ...

Engineer to design, build, and deploy production-ready Generative AI systems. Role You will own the full lifecycle of GenAI applications, working with LLMs, RAG architectures, and agentic frameworks to deliver scalable, business-focused solutions. Responsibilities Build and deploy GenAI and RAG systems Develop agentic AI workflows Design prompts … clients and technical teams Requirements 1-3 years deploying LLM/GenAI systems in production Strong Python skills Experience with cloud platforms Knowledge of RAG, vector databases, and LLM frameworks Git and CI/CD experience Experience with LangChain and LangGraph AWS experience IT/Technology-related university degree (minimum ...

teams. Design and deliver Voice AI/Agentic IVR solutions leveraging: Google CES/CXAS, Dialogflow CX/CCAI Vertex AI (LLMs, RAG, agent frameworks) Define and implement enterprise data architecture for Voice AI: Conversation data pipelines (real-time + batch) Integration with data lakes, warehouses (BigQuery) Customer … contextual data enablement Build RAG-based knowledge systems integrating structured and unstructured banking data. Architect data-driven decisioning for voice agents (personalization, next-best action, fraud detection signals). Ensure integration with core banking, CRM, and analytics platforms . Establish data governance, lineage, quality, and compliance frameworks (GDPR ...

production-ready. Technology The successful candidate will work with modern cloud platforms (AWS or similar), vector databases, knowledge graphs, retrieval systems, embeddings, RAG architectures, data pipelines, APIs and contemporary AI tooling. What they're looking for Strong commercial experience in senior data engineering, knowledge engineering or a closely … technologies and architectures rather than defaulting to a single solution. Strong software engineering and cloud platform fundamentals. Nice to have Experience with production-scale RAG architectures and retrieval evaluation. Knowledge of emerging open knowledge standards and interoperable knowledge formats. Experience with context engineering for AI agents ...