526 to 550 of 886 Retrieval-Augmented Generation Jobs in England

applications in production environments. Experience with vision‐language models or multimodal AI over images, video, inspection evidence, diagrams, screenshots, or technical records. Experience with RAG over structured and unstructured data. Familiarity with observability, log analysis, incident response, support tooling, runbooks, or developer tools. Experience building workflow UIs where AI assists ...

collaborate with product teams to deliver reliable AI tooling. The role requires strong Python, expertise with LLM frameworks, FastAPI, and Kubernetes, plus experience with RAG and tool orchestration in an enterprise setting. #J-18808-Ljbffr ...

/GCP). Ability to standardise and influence across teams. Nice to Have Lakeflow connectors, RBAC/masking, GenAI (Mosaic AI, Vector Search, RAG), Lakebase. #J-18808-Ljbffr ...

include Docker, Kubernetes, Azure, Infrastructure as Code, enterprise APIs, identity and access management, observability and modern DevOps practices. Exposure to AI platforms, LLM integration, RAG, agent frameworks or accelerated compute would be valuable, but we are primarily looking for strong architectural breadth and the ability to deliver complex technology ...

Science, Machine Learning, Data Science, or a related field Have impactful industry experience building GenAI-focused applications with technologies including Agents, reasoning models, and RAG Have high-level familiarity with the architecture and operation of large language models Have personally implemented models in PyTorch, Jax, or TensorFlow Have a strong ...

engineers and act as the technical escalation point within the delivery team Nice-to-have experience includes: • Mosaic AI, Vector Search, model serving or RAG pipelines • Lakeflow ingestion connectors • Row-level security, column masking and sensitive data handling through Unity Catalog • Databricks performance optimisation, including Photon, cluster sizing, partitioning ...

/agile development methodologies * Proficient in using AI in your day-to-day tasks, and incorporating AI into your software solutions (LLMs, agentic flows, RAG, tool use, MCP etc.) * The ability to mentor a dev team in the effective application of AI throughout the SDLC Nice to have: * AWS certification ...

launch and architecting AI systems on cloud platforms (e.g., 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 with customers. Active, or the ability to obtain, a Security Check ...

launch and architecting AI systems on cloud platforms (e.g., 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 with customers. Active, or the ability to obtain, a Security Check ...

experience in APIs, integrations and distributed systems Proven experience designing, building and delivering solutions into production environments Experience with AI architecture and patterns, including RAG, tool calling and multi-agent orchestration Experience building or deploying AI/LLM-powered solutions and workflows Full-stack software engineering skills, particularly Python, APIs ...

launch and architecting AI systems on cloud platforms (e.g., 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 with customers. Active, or the ability to obtain, a Security Check ...

Qualifications 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. Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines. ...

environment. Proven experience leading complex software implementation or systems integration programs. Strong understanding of the software development lifecycle and AI solution deployment (LLMs, RAG systems, prompt engineering, vector databases, etc.). Proficiency with project planning and collaboration tools (e.g., Jira, Asana, Smartsheet). Technical familiarity with Python, APIs, and enterprise ...

launch and architecting AI systems on cloud platforms (e.g., 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 with customers. Active, or the ability to obtain, a Security Check ...

experience building and deploying production-grade software in Python; you write clean, testable, observable, scalable code You've built and deployed highly performant RAG and agentic applications, including agents that plan and execute multi-step tasks using patterns like ReAct or Plan-and-Execute You're deeply familiar with ...

hands-on experience building and deploying production-grade software in Python; you write clean, testable, observable, scalable codeYou've built and deployed highly performant RAG and agentic applications, including agents that plan and execute multi-step tasks using patterns like ReAct or Plan-and-ExecuteYou're deeply familiar with ...

essential. AI and ML platform architecture: experience designing AI/ML data infrastructure - including feature stores, model training pipelines, inference serving, and GenAI/RAG architectures grounded in proprietary content. Lakehouse and modern data platform design: strong knowledge of open table formats (Apache Iceberg, Delta Lake), zone architecture (landing, cleansed ...

experience in leading cross-functional teams and delivering projects on a time-bounded schedulehave a strong understanding of GenAI fundamentals (LLMs, prompt engineering, RAG, fine-tuning, hallucinations, bias, accuracy, etc.)have worked with model APIs like OpenAI, Claude, or Mistralhave built or iterated on customer journeys using low-code/ ...

squad's needs evolve. Requirements: Engineering experience: 3+ years in software engineering, with hands-on experience building LLM-powered applications in production (RAG, tool-augmented agents, or agentic workflows). Education: BS in Engineering, Computer Science, or equivalent. Ways of working: Comfortable working autonomously within a defined problem … doesn't make sense, and delivering in short, time-boxed cycles where validated outcomes matter more than perfect solutions. Agentic AI/LLMs: Building RAG pipelines, tool-augmented agents, and agentic workflows; familiar with prompt engineering, context management, evaluation, and observability. Agent fundamentals: Understands how agents use memory ...

production code while guiding technical direction Deep hands‐on expertise building enterprise AI applications using large language models, AI agents, retrieval‐augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype to production … platform capabilities that support real‐time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval‐augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable for practical implementation ...

Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. You'll work inside … eventually run these systems without long-term reliance on external suppliers. Key Responsibilities Hands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than ...

solving some of the most exciting challenges and problem solving in enterprise AI rollout, spanning enterprise data pipelines, multi-agent orchestration/workflows and RAG/SLM finetuning. Responsibilities Lead the technical solution design and implementation of large-scale Agentic and Knowledge AI solutions for key enterprise clients. … solution delivery through reusable assets, frameworks, and documentation. Champion observability, monitoring, versioning and telemetry to ensure trustworthy and auditable AI agents. Stay current on RAG, LLM/SLM advancements, Agentic AI tooling, vector/graph databases, retrieval optimizations, multi-modal systems, evaluation frameworks, GPU/Infra trends, fine ...

Lead the implementation of AI-enabled applications and platform capabilities. Establish reusable patterns, frameworks, and best practices for AI engineering. Design and oversee LLM, RAG, and Agentic AI integrations within enterprise environments. Collaborate with architects and engineering teams to ensure scalable, secure, and maintainable solutions. Define standards for AI observability … applications on Azure, AWS, or GCP. Strong knowledge of Kubernetes, Microservices, APIs, Event-Driven Architecture, and DevOps practices. Hands-on experience with LLM integration, RAG architectures, AI orchestration frameworks, and Agentic AI solutions. Experience with AI frameworks and tools such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent. Strong ...

audit-ready evidence generation. AI Enablement Implement AI/ML components within the model workflow, including LLM-based automation for model documentation generation and updating, intelligent data quality checks, and workflow assistance. Integrate AI tooling into a controlled, auditable, production-grade environment, with appropriate testing, monitoring, and governance … Solid AI/ML knowledge, including practical experience with machine learning libraries and/or LLM-based application development (e.g., API integration, prompt engineering, RAG-style document workflows). Strong experience with test automation, CI/CD, and modern software engineering practices (Git, code review, containerization). Experience working with ...

periodic reviews, and audit-ready evidence generation.AI EnablementImplement AI/ML components within the model workflow, including LLM-based automation for model documentation generation and updating, intelligent data quality checks, and workflow assistance.Integrate AI tooling into a controlled, auditable, production-grade environment, with appropriate testing, monitoring, and governance … preferred.Solid AI/ML knowledge, including practical experience with machine learning libraries and/or LLM-based application development (e.g., API integration, prompt engineering, RAG-style document workflows).Strong experience with test automation, CI/CD, and modern software engineering practices (Git, code review, containerization).Experience working with large datasets ...