276 to 300 of 301 Retrieval-Augmented Generation Jobs in the UK

working on: • Production pipelines from financial data vendors • Data flow optimisation across the business • Reliability, alerting and latency improvements • AI capability, including RAG pipelines and OCR-based document extraction • AWS infrastructure managed with Terraform • Close collaboration with both technical and non-technical stakeholders What they’re looking for: • Strong Python … technical users • Comfortable using AI engineering tools such as Cursor, Claude Code or Codex Bonus points for: • Financial services experience • MongoDB • Vendor API integrations • RAG/LLM tooling You’ll suit this role if you like autonomy, enjoy solving messy business problems, and get genuinely annoyed when a process ...

machine learning or software engineering environments. You will work on a broad range of AI challenges spanning multimodal AI, machine learning, computer vision, LLMs, RAG architectures, transformer models, audio processing and intelligent automation features within a highly collaborative engineering environment. Location: Cambridge, UK. Hybrid working model - minimum of 4 days … Kotlin, TypeScript and React, prior experience with these technologies is not required Interest and exposure across AI technologies such as machine learning, LLMs, RAG systems, computer vision, multimodal AI or transformer architectures Ability to work across both research/prototyping and production engineering Strong problem-solving ability and analytical thinking ...

party platforms Develop microservices, wrappers, connectors, and service interfaces to support solution components Support applied AI development including LLM services, speech services, classification models, RAG pipelines, and AI-enabled application features Prepare and maintain demo-ready technical builds for internal and customer-facing demonstrations Support the preparation of technical handoff … environment Excellent communication skills to articulate technical issues and blockers clearly Desirable Skills: Experience with AI/ML tools, LLMs, speech services, RAG systems, or AI APIs Experience with Unreal Engine, Unity, or other real-time 3D development environments Familiarity with Docker, FastAPI, REST APIs, Git, and deployment workflows Experience ...

operational software — turning fragmented operational data into structured, trusted intelligence. We're focused on difficult, practical problems: Text-to-SQL against real enterprise schemas RAG over messy operational data Screen recorded workflow mapping Workflow and knowledge tooling that operational teams will actually adopt The platform is being developed alongside … t. Tech you'll touch Laravel · Livewire · Alpine · Tailwind · Python · SQL Server (T-SQL) · PostgreSQL · Power BI/DAX · Docker · GitHub · LLM integrations · RAG · embeddings · Claude Code & Cursor daily Logistics Location: Fully remote, UK Hours: Full-time Type: Contract-to-perm. Outside IR35. Salary: £70,000–£80,000 on conversion ...

client teams to design, build and deploy AI driven solutions on the platform Translate complex research and business questions into production ready LLM and RAG workflows Debug, optimise and scale LLM applications in real world, client facing environments Build and integrate data pipelines and services across cloud platforms and modern … Your Skills and Experience Strong commercial engineering experience in AI, machine learning or data focused roles Hands on experience with retrieval augmented generation and production LLM workflows Confidence debugging and improving end to end LLM systems in live environments Experience working with modern data ...

product challenges Responsibilties Design, build and deploy production-grade AI applications and services Develop intelligent workflows using LLMs, agentic architectures and orchestration frameworks Build RAG systems, knowledge retrieval platforms and AI-powered tooling Integrate AI applications with internal systems, business platforms and external services Develop scalable backend services … Your Experience Strong software engineering foundations Experience building and deploying AI products into production environments Python and modern backend engineering experience Experience with LLMs, RAG architectures and agentic AI systems MCP or similar protocols for connecting AI systems with tools and external services Strong product thinking and commercial awareness Experience ...

real-world impact at scale 🟠 Responsibilities Design, build and deploy AI-powered products and applications Develop production-grade LLM solutions and agentic workflows Build RAG architectures and knowledge retrieval systems Integrate AI applications with internal platforms, tools and business systems Identify opportunities where AI can create measurable commercial … Your Experience Strong software engineering foundations Experience building and deploying AI products into production environments Python and modern backend engineering experience Experience with LLMs, RAG architectures and agentic AI systems MCP or similar protocols for connecting AI systems with tools and external services Strong product thinking and commercial awareness Experience ...

Financial Services team. You'll be at the intersection of Agentic AI and Zero Trust , building the secure backbone for the next generation of AWS Bedrock deployments. The Baseline FinServ Pro: You know the UK landscape inside out- DORA , FCA Operational Resilience , and the EU AI Act aren … Hands-on experience with AWS Bedrock (Agents, Knowledge Bases, Guardrails, and the full model lifecycle). AI/ML Foundations: Deep understanding of FMs, RAG , non-deterministic agents, and complex tool-use. Secure AI: Expert-level knowledge of OWASP LLM Top 10 and Agentic AI threats . Familiarity with NIST ...

experimentation’s sake. Scope of Role Design, build and deploy AI-powered products and applications Develop production-grade LLM solutions and agentic workflows Build RAG architectures and knowledge retrieval systems Integrate AI applications with internal platforms, tools and business systems Identify opportunities where AI can create measurable commercial … Your Experience Strong software engineering foundations Experience building and deploying AI products into production environments Python and modern backend engineering experience Experience with LLMs, RAG architectures and agentic AI systems MCP or similar protocols for connecting AI systems with tools and external services Strong product thinking and commercial awareness Experience ...

Guide teams in building highly available, secure, and resilient distributed systems. AI Platform & Infrastructure Build enterprise-grade AI platforms supporting model serving, vector databases, RAG pipelines, agent orchestration, and AI observability. Define standards for AI governance, model lifecycle management, prompt engineering, evaluation frameworks, and responsible AI practices. Establish scalable infrastructure … building Agentic AI products from 0→1 and taking them into production environments. Deep expertise in: Agentic AI Architectures Multi-Agent Systems LLM Orchestration RAG Architectures AI Workflow Automation Autonomous Decision Systems Prompt Engineering & Evaluation Frameworks AI Observability & Monitoring Experience deploying production-grade AI systems serving large-scale customer ...

constraints Designing agentic AI architectures across LLMs, data, APIs, workflows, and enterprise tools Building and guiding hands-on engineering work across Python, Node.js, TypeScript, RAG, agents, and integrations Helping clients move from “AI ideas” to working systems Contributing to Conquer AI’s own reusable product capability Supporting other engineers through … standards What we’re looking for: Strong software engineering background Experience leading technical delivery or owning complex engineering workstreams Hands-on experience with LLMs, RAG, agentic AI, workflow automation, or applied ML Strong backend capability, ideally with Python, Node.js, or TypeScript Ability to communicate clearly with senior clients Comfort operating ...

strong technical depth with delivery leadership and stakeholder management. You’ll implement AI and agentic systems across enterprise clients, designing scalable architectures using LLMs, RAG pipelines, and agentic workflows, while working closely with engineering and product teams to ensure successful outcomes. The Candidate Key attributes of the suitable Lead Data … Strong background delivering production-grade solutions, ideally within consulting or product-led environments Agentic AI & Modern AI Systems Experience – Hands-on exposure to LLMs, RAG pipelines, and agentic frameworks Delivery Leadership & Client Ownership – Comfortable leading teams while remaining technically involved and managing client relationships post-sale ...

business problem, close to the technical design, and close to the delivery. What you’ll work on: Agentic AI systems LLM-powered enterprise applications RAG pipelines and knowledge systems Document intelligence and workflow automation AI-driven operational systems Data pipelines and orchestration API and enterprise system integrations Production AI systems … Move quickly from ambiguity to working systems We’re looking for: Strong software engineering and systems thinking capability Hands-on experience with AI, LLMs, RAG, ML systems, agents, or workflow automation Experience designing production systems and integrations Strong understanding of APIs, orchestration, data pipelines, and backend systems Ability to operate ...

Snowflake, S3 and Postgres platform into an AI-ready ecosystem Building scalable data acquisition and ingestion pipelines across multiple data sources Developing LLM and RAG-powered services and experiences Creating new Data Production and Data Discovery capabilities Partnering with Analytics teams to deliver clean, accessible and valuable data Managing … foundations and a genuine desire to keep learning. Nice to Have Experience with: Browser automation and web scraping tools such as Playwright or Puppeteer RAG evaluation frameworks Private or self-hosted AI models Working across both cloud-based and private AI environments Benefits Why Join ECA? This is an opportunity ...

client teams to design, build and deploy AI driven solutions on the platform Translate complex research and business questions into production ready LLM and RAG workflows Debug, optimise and scale LLM applications in real world, client facing environments Build and integrate data pipelines and services across cloud platforms and modern … Your Skills and Experience Strong commercial engineering experience in AI, machine learning or data focused roles Hands on experience with retrieval augmented generation and production LLM workflows Confidence debugging and improving end to end LLM systems in live environments Experience working with modern data ...

graphs, not just conceptual ontology work. You’ll be responsible for shaping a graph and ontology platform that powers: • AI retrieval and RAG workflows • Entity linkage and reasoning systems • Cross-domain and temporal knowledge modeling • Regulatory and compliance intelligence products • Agentic AI applications The role combines architecture, hands … experience includes: • Strong Neo4j and Cypher expertise in production environments • Deep ontology/taxonomy modeling experience • Python engineering skills • Knowledge graph integration with LLMs, RAG, or vector search systems • Experience balancing formal semantics with pragmatic implementation • Ability to lead technically while remaining hands-on Additional experience in areas such ...

solutions remain simple, reliable, and aligned with business needs. Data & Knowledge Systems Design company-wide knowledge and retrieval systems. Build and optimize RAG and hybrid-search architectures. Integrate structured and unstructured enterprise data sources, including: Slack,Atlassian/Jira, Meeting transcripts, Internal documentation, Team directories, Corporate databases. Engineering … deploying AI/LLM-powered products used by real users. Strong experience with multi-agent systems and AI orchestration frameworks. Deep understanding of RAG architectures, Vector databases, Hybrid search, Knowledge management systems, LLM evaluation and quality measurement. Experience integrating AI systems into enterprise environments. Strong software engineering background with ...

LLMs, data, APIs, workflows, and systems Stay close to the engineering, not just the architecture Build and guide solutions using Python, Node.js, TypeScript, LLMs, RAG, agents, and integrations Help shape greenfield AI projects from idea through to production Work across client delivery and Conquer’s own AI product development … looking for someone who has: Strong hands-on software engineering experience Experience building real AI, LLM, RAG, agentic, or automation systems Strong backend engineering capability, ideally across Python, Node.js, or TypeScript Comfort working with APIs, data flows, integrations, and enterprise systems The ability to speak clearly with clients and translate ...

already deploying autonomous AI systems into production inside major global enterprises. We’re talking: → AI agents executing workflows end-to-end → Multi-agent systems → RAG + reasoning systems → Real operational impact → Production-grade AI, not demos They’re hiring engineers who can bridge deep technical capability with real-world business … work directly with enterprise customers to design and deploy AI systems that actually do things — not just generate outputs. Ideal backgrounds: LLMs/RAG/MCP/agents applied AI engineering strong Python engineering fundamentals customer-facing or forward deployed experience builders who move fast and like ownership Hybrid London ...

Agents, and Denial-of-Wallet-into rigorous test cases. AWS Bedrock Mastery: Overseeing the secure model lifecycle, including the configuration of Guardrails, Knowledge Bases (RAG), and Agentic tool-use. Regulatory Stewardship: Mapping the firm's safeguard catalogue against the specific demands of DORA, FCA Operational Resilience … appetite. Essential Skill Proven track record in UK Financial Services with deep knowledge of FCA compliance and DORA. Solid grasp of Foundation Models (FMs), RAG, and the risks of non-deterministic agentic behavior. Mastery of OWASP LLM Top 10 and the NIST AI Risk Management Framework. Hands-on experience securing ...

opportunity to influence the technical direction of a newly formed AI Lab. Key Responsibilities • Build and deploy production-grade LLM systems • Design and optimise RAG and retrieval workflows • Fine-tune models for tone, style, and output quality • Develop audio-first AI products using TTS/STT workflows • Build … greenfield environment Key Details • Salary: Up to ~£120k base • Working model: Hybrid (3+ days/week in London) • Stack: Python, HuggingFace, LangChain, RAG, multimodal AI, TTS/STT, evaluation frameworks • Sponsorship: To be confirmed Interested? Please apply below. ...

engineering and data science teams to understand their environments and design robust AI driven integrations. You will design and build solutions using retrieval systems, RAG architectures, agent based workflows, and APIs. You will contribute to technical scoping during pre-sales, helping shape feasible and scalable solutions. You will … Your Skills and Experience Strong commercial experience deploying NLP or LLM powered systems into production environments. Hands-on experience with retrieval augmented generation, vector databases, and retrieval or recommendation systems. Practical experience building, debugging, and operating end to end LLM workflows. ...

users ranging from highly technical teams to more non technical stakeholders Designing and building AI driven workflows, including LLM and retrieval augmented generation use cases Integrating knowledge graph data into customer environments using APIs, graph databases or analytical platforms Partnering with a solutions consultant … commercial experience building and deploying AI or LLM powered systems into production Hands on experience designing, building and debugging LLM workflows, including familiarity with RAG approaches Experience working with modern data platforms and warehouses such as Snowflake or BigQuery Exposure to graph databases and knowledge graphs is highly desirable, (Neo4J ...

Location Manchester, UK (Hybrid - 3 days per week in office) About ServisBOT ServisBOT is building the next generation of AI-driven enterprise automation for regulated industries — a secure, compliant SaaS platform where AI and humans work together to deliver outcomes our clients can trust. We partner with global … implementation in client environments and ensuring we deliver measurable value. You’ll also build confidence in key AI concepts like prompt engineering, LLMs, and RAG, helping translate customer needs into practical, trustworthy AI-enabled solutions. Key Responsibilities Problem Definition & Discovery Support discovery activities for product initiatives and customer engagements Help ...

product team work: prioritisation, sprint planning, feedback loops, release management, post-launch reviews, and clear trade-off calls. • Shape our AI experiences — things like RAG pipelines, multimodal user experiences, and LLM-powered experiences that actually work in production — from a product and customer-value angle. • Embed Ignitus into the flow … offs. • Experience leading technical projects or teams, even if you were not the direct line manager. • Comfort working with AI products, LLM-powered experiences, RAG, vector databases, or complex data-driven products — and the curiosity to go deep where needed. • Excellent product execution skills: prioritisation, roadmap management, specs, release planning ...