26 to 50 of 50 Retrieval-Augmented Generation Jobs in Central London

Requirements AWS Bedrock Expert: Hands-on experience with Bedrock Agents, Knowledge Bases, and model lifecycle management. AI/ML Depth: Strong grasp of FMs, RAG, tool-use, and the failure modes of agentic workflows. Security & Compliance: Deep knowledge of NIST AI RMF, OWASP LLM Top 10, and UK/ ...

into CI/CD pipelines and wider SDLC activities. Should have: Practical exposure to testing AI/ML/LLM‐based systems (e.g. chatbots, RAG applications, AI‐assisted workflows) and an interest in deepening this. Familiarity with approaches to evaluating LLM outputs (for example, semantic‐similarity based methods or using ...

small team, wearing multiple hats, and moving at speed without heavy process. AI/LLM experience (a plus) : Experience building with AI agents, RAG systems, or LLM-powered features is valuable but not required. End-to-end ownership mindset : You've shipped something meaningful on your own—a project, open ...

this Senior Consultant role, you will operate as a hands-on engineer within a squad, designing and implementing AI capabilities such as agents, RAG pipelines and supporting services while ensuring solutions meet enterprise standards for reliability, security and governance. What you'll do Client-facing engineering & delivery Collaborate with client … audiences. Support the adoption of delivered solutions through testing, iteration and feedback cycles. Solution design & implementation Design and implement AI-enabled components such as RAG pipelines, agents and supporting microservices. Integrate solutions with enterprise data sources, APIs and existing platforms while maintaining reliability and security standards. Apply responsible-AI guardrails ...

adaptive AI architectures Implementing prompt engineering at production scale Developing and managing multi-agent workflows Optimising retrieval systems and memory strategies (RAG architectures) Requirements 5+ years commercial experience with Python/PHP Proven experience delivering production-grade AI systems (not just prototypes) Hands-on experience building autonomous systems ...

Python and FastAPI on the backend, React and TypeScript on the frontend, Postgres, ElasticSearch and vector databases for data, and LLMs, agents and RAG across the AI layer. What they're looking for Has built and managed an engineering team through a high-growth scaling phase, not just inherited ...

deliver solutions that create measurable commercial impact. What you'll be doing • Designing enterprise AI and GenAI architectures • Building scalable solutions across LLMs, RAG and Agentic AI • Defining AI platform, governance and deployment strategies • Advising senior stakeholders on AI adoption and transformation • Working across engineering, cloud, data and business teams … Driving AI from proof of concept to enterprise-scale delivery Technology Python, LLMs, RAG, Agentic AI LangChain, LangGraph Vector Search & Semantic Retrieval MLOps & LLMOps AWS, Azure, GCP, Databricks Kubernetes & Cloud-Native Platforms We're looking for • Experience designing enterprise AI, ML or data platforms • Strong cloud and modern ...

turning ambition into actionable strategy across the business. You’ll work at the intersection of business and technology, translating AI capabilities (LLMs, document intelligence, RAG) into commercially meaningful initiatives across domains such as customer, operations, and risk. Rather than delivering isolated use cases, your focus will be on building ...

extend platform components—APIs, tooling, and bespoke logic Architecture & Standards Architect robust, scalable, and compliant Agentic AI solutions across industries Lead multi-agent orchestration, RAG pipelines, and LLM/SLM integration design Champion observability, telemetry, and auditable AI standards Build and maintain reusable frameworks and asset libraries Team & Product Help …/Vue, SQL, REST/GraphQL DevOps proficiency: Docker, Kubernetes, CI/CD, and major cloud platforms Expertise in LLMs, Prompt Engineering, Vector DBs, RAG pipelines, and Agentic orchestration frameworks Agentic/Integrations: MCP, Salesforce/SAP/Other Excellent communication with both technical and executive stakeholders Mentorship capability ...

TypeScript Contribute to scalable, distributed systems and cloud architecture (AWS) Work with structured and unstructured data across varied storage solutions Build and integrate RAG pipelines and AI-driven workflows Implement LLM integration patterns and multi-step orchestration Collaborate with cross-functional teams to deliver robust, high-quality systems What They ...

enterprise clients. We work with organisations to understand their workflows, identify where AI can automate or augment work, and build practical systems across LLMs, RAG, workflow automation, document intelligence, operational copilots, data extraction, and enterprise integrations. Our projects move quickly, so delivery is critical. We need someone who can help ...

platform and infrastructure engineering role - not a data science or ML engineering position. You'll own the runtime, infrastructure, and operational layers that RAG pipelines, LLM orchestration, vector search, and evaluation workflows run on, across AWS and Databricks. The focus is on building scalable, observable, secure, and cost-efficient platform … infrastructure (OpenSearch, Algolia, AWS Bedrock Knowledge Bases) and embedding pipelines Build and maintain CI/CD pipelines for inference services, retrievers, ingestion workflows, and RAG components Implement observability across AI workloads using CloudWatch, MLflow, and OpenTelemetry - covering latency, throughput, cost, and system health Apply secure-by-design principles including ...

this is the clearest indicator of someone who has shipped LLM systems in production. • Deep, current LLM engineering practice. System-level prompt design, RAG, structured generation, fine-tuning trade-offs (SFT, DPO, LoRA), and working knowledge of the frontier model landscape. • LLM safety and security. Prompt injection mitigation ...

London. Known for their entrepreneurial culture and deep expertise across financial services, this is an exceptional opportunity to architect and deliver next-generation AI systems at the heart of some of the world's most complex financial institutions. You'll take a hands-on leadership role designing … that bring together multi-modal LLMs. Building agentic workflows that connect AI agents to live data sources and APIs, drawing on prompt engineering and RAG to drive accuracy and relevance Optimising and shipping large language and multi-modal models into production, balancing performance, latency, and reliability at enterprise scale Developing ...

across products and enterprise transformation initiatives. What You'll Do Design and deploy AI agents and agentic workflows powered by LLMs Build systems using RAG, reasoning, planning, memory, and tool orchestration Develop multi-step AI solutions capable of autonomous decision-making and tool usage Work with MCP-style architectures … applications into production Hands-on expertise with LangChain, LangGraph, or similar frameworks Demonstrable experience building Agentic AI systems and AI agents Strong understanding of RAG architectures and LLM optimisation Experience with FastAPI, MCP/FastMCP, or similar technologies Familiarity with cloud-native AI deployments and data-driven applications Important Applications ...

growth of the company. You’ll take responsibility for key parts of the AI stack, including: Architecting and deploying multi-agent systems Building scalable RAG pipelines and retrieval systems Creating evaluation frameworks (performance, safety, reliability) Shipping AI-driven products used in real-world enterprise settings Alongside this … design: orchestration, memory, deployment, monitoring – Exposure to tools such as LangGraph, ReAct, CoT — or building similar systems from scratch – Familiar with PyTorch/TensorFlow, RAG architectures, vector databases, modern cloud infra – Experience in startup or consulting environments is a plus – Comfortable working autonomously in fast-moving, sometimes ambiguous settings With ...

reusable agent components and playbooks Proactively surface new automation opportunities across the business Key Skills Python & LLM agent frameworks (LangChain, CrewAI, LlamaIndex) Prompt engineering & RAG pipelines Business analysis & stakeholder management Process mapping & workshop facilitation Microsoft 365 & Microsoft Copilot No-code/low-code tooling (Zapier, Make) Desirable Skills Experience … etc. Strong enough technically to assess feasibility and scope what is buildable; commercially sharp enough to prioritise what is worth building. Familiarity with LLMs, RAG pipelines, tool/function calling, and integrating AI into real business workflows. Proven ability to lead discovery workshops and translate complex business problems into clear ...

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. ...

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 ...

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 ...

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 ...

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 ...

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 ...