126 to 148 of 148 Retrieval-Augmented Generation Jobs in London

highly regulated financial environment? This is a genuinely exciting opportunity to join a top banking organisation investing heavily in automation, AI and next-generation engineering capabilities. You'll be building full-stack applications, AI-powered tools, automations and workflow solutions that directly improve business operations and user productivity … enjoys combining modern software engineering with emerging AI technologies. Tech & Projects Include: Python (FastAPI/Flask) JavaScript, REST APIs & SQL Server AI & LLM applications RAG pipelines & agentic AI workflows Locally hosted open-source LLMs Microsoft Power Platform LangChain, LangGraph, Semantic Kernel & Copilot Studio exposure Why this role stands out: Real ...

commercially effective from concept through to production. Key areas of focus include: • Enterprise AI and machine learning architecture • Databricks, AWS, and Snowflake ecosystems • RAG, vector databases, and agentic AI orchestration • Cloud-native, API-driven, and event-driven architectures • FinOps and cloud cost optimisation • AI governance, security, and compliance • Cross-functional … reinsurance sector experience advantageous but not essential This represents an excellent opportunity to join a highly respected global organisation investing significantly in next-generation AI capability and long-term technology transformation. For a confidential discussion, please feel free to contact me directly. ...

close to clients, close to the problem, and close to the code. What you’ll work on: Agentic AI workflows LLM-powered applications RAG and knowledge systems Document extraction and classification Workflow automation API and system integrations Client-facing prototypes and production builds Internal Conquer AI product development What … secure AI applications Move quickly from concept to working product We’re looking for: Strong software engineering experience Hands-on experience with AI, LLMs, RAG, agents, ML systems, or automation Good backend engineering capability Experience with APIs, data, integrations, and production systems Clear communication and client-facing confidence A bias ...

enterprise Agentic AI platforms. You will move beyond traditional software QA to build continuous safety pipelines, ensuring our non-deterministic LLM agents, RAG systems, and tool integrations are secure, resilient, and compliant before production release. Key Responsibilities Automated Adversarial Testing: Build and integrate automated red teaming suites into CI/… within FCA, DORA, or EU AI Act frameworks. AWS Bedrock Ecosystem: Hands-on experience configuring, testing, and bypassing Bedrock Guardrails, Agents, and Knowledge Bases (RAG) . AI Security & Fundamentals: Solid understanding of Foundation Models, tool use (function calling), OWASP LLM Top 10 , and NIST AI RMF . Automation Stack: Strong ...

enterprise Agentic AI platforms. You will move beyond traditional software QA to build continuous safety pipelines, ensuring our non-deterministic LLM agents, RAG systems, and tool integrations are secure, resilient, and compliant before production release. Key Responsibilities Automated Adversarial Testing: Build and integrate automated red teaming suites into CI/… within FCA, DORA, or EU AI Act frameworks. AWS Bedrock Ecosystem: Hands-on experience configuring, testing, and bypassing Bedrock Guardrails, Agents, and Knowledge Bases (RAG) . AI Security & Fundamentals: Solid understanding of Foundation Models, tool use (function calling), OWASP LLM Top 10 , and NIST AI RMF . Automation Stack: Strong ...

Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques like parameterefficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generation pipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage and retrieval ...

build and deliver AI-powered product features end-to-end. A key part of the role involves integrating LLM functionality into production systems, including RAG pipelines, retrieval systems, agent workflows, and AI-assisted automation. Required Experience Strong commercial experience with TypeScript Strong backend engineering experience with Node.js Experience … building scalable APIs and backend services Commercial experience working with LLMs in production Hands-on experience with RAG pipelines and retrieval systems Experience with vector databases, embeddings, and AI orchestration tooling Experience integrating AI functionality into live products Cloud platform experience (GCP preferred) Product-focused mindset with experience ...

rigor, and building an inclusive, collaborative culture. Deliver advanced AI and knowledge-discovery systems across the full lifecycle, from experimentation through production, including LLMs, RAG, search, and domain-enriched AI solutions. Drive evaluation and AI quality by establishing robust frameworks, metrics, experimentation practices, and responsible AI standards. Influence across product … data science methods, including machine learning, experimentation, deep learning, generative AI, and production AI systems. Hands-on experience delivering AI-powered products, including LLMs, RAG, semantic search, embeddings, agentic workflows, and knowledge-driven systems. Proven success leading and developing technical teams in complex product, platform, or research environments. Experience working ...

agents and automated decisioning can be embedded into core operations. AI-ready enterprise knowledge architecture - building the structured business meaning required for GenAI, RAG, knowledge graphs, agentic AI, explainable analytics and intelligent process orchestration. This is not a pure data modelling role. It is a business architecture, domain modelling, semantic … simply system-level data structures. 5. Knowledge graph and AI enablement Define insurance entities and relationships for knowledge graphs and graph-based reasoning. Support RAG and GenAI solutions with governed domain knowledge and contextual enrichment. Help define AI agent memory, context, guardrails, decision boundaries, explainability and traceability. Create reusable ontology ...

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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...

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 ...

Inside IR35) Scope of work : The consumption layer that determines how agents query and use the graph. RESPONSIBILITIES Defines tool-calling schemas, RAG pipelines, and context strategies. Builds hallucination guardrails and agent evaluation harnesses. Integrates the graph with enterprise agentic platforms. KEY SKILLS LLM integration RAG architecture Tool/function ...