351 to 375 of 842 Retrieval-Augmented Generation Jobs in the UK

API. Proven track record of delivering production software on client-facing projects, ideally within a consultancy or professional services environment. Experience with prompt engineering, RAG architectures, or agentic AI development at scale. Familiarity with AI-assisted development tools such as GitHub Copilot, Claude, and Cursor. Strong experience with Azure cloud ...

productive teams Experience leading teams in fast‐growing startups or enterprise software companies Knowledge of financial planning, analytics, or business intelligence domains Experience with RAG systems, vector databases, and semantic search Familiarity with model fine‐tuning, evaluation, and continuous improvement workflows Background in distributed systems, microservices, or cloud‐native architectures ...

modern data architectures, including data lakes/warehouses, governance frameworks, and master/metadata management. Hands‐on experience with Generative AI and LLMs, including RAG, embeddings, vector search, GraphRAG, prompt engineering, and agent frameworks (e.g. LangChain, LlamaIndex). Strong stakeholder skills, able to quickly understand complex business domains and communicate ...

solutions, from discovery through to production. Develop and evaluate statistical models, machine learning systems, and Generative AI solutions, including LLM‐based and RAG‐enabled approaches. Support the operationalisation of models and AI solutions, including deployment, monitoring, governance, and lifecycle management. Translate complex analytical outputs into clear, actionable insights for both ...

Skills: Proven expertise and hands-on delivery of AI/ML in financial services Strong technical depth in GenAI/LLMs, prompt engineering, RAG architecture, fine-tuning, vector databases Knowledge of evaluation metrics for LLMs and safety/guardrail techniques Understanding of supervised learning and data engineering fundamentals Leadership experience ...

Practice eval‐driven development: Build and own the evaluation harnesses that measure accuracy, hallucination, and performance before anything ships. Solve performance at scale: Tackle RAG latency optimization, context‐window management, and intelligent information routing under real production load. Build observability: Ship tooling that gives the team real‐time insight into ...

productive teams Experience leading teams in fast-growing startups or enterprise software companies Knowledge of financial planning, analytics, or business intelligence domains Experience with RAG systems, vector databases, and semantic search Familiarity with model fine-tuning, evaluation, and continuous improvement workflows Background in distributed systems, microservices, or cloud-native architectures ...

models (DAX and Power Query) Exposure to ML/AI concepts (e.g. scikit-learn, TensorFlow) or an interest in preparing data for LLMs, RAG architectures or predictive models Experience with Docker and/or Terraform (desirable) What You Will Learn & What Liquidline Can Offer You Being a part of Liquidline ...

acceptance testing and contract-style testing Observability, monitoring and production debugging Security, compliance, auditability or access-control concerns in product development Prompting, agent workflows, RAG systems or AI-assisted internal tooling Building personal products, prototypes, automation scripts or internal tools Join a mission-driven team addressing real pain points ...

/CD pipelines that measure accuracy, hallucination, and performance before any model or system ships. Optimize Performance at Scale: Strategically tackle complex challenges like RAG latency optimization, context-window management, and intelligent information routing under enterprise-level production load. Build Observability & Governance: Define and build real-time monitoring, tracing ...

Evaluation techniques Proven experience working end-to-end on Software solutions and CI/CD Pipelines Proficiency in Python and pydantic validation Experience building RAG systems, Prompt engineering, Data processing Preferred Skills Understanding of optimizing both CPU-bound and GPU-bound workloads. Knowledge of monitoring and observability tools (e.g. Prometheus ...

capabilities, and appropriate use cases. Strong understanding of AI, ML and LLM concepts, including prompt engineering, prompt grounding, iterative loop techniques, context windows, embeddings, RAG, agentic workflows. Proven ability to integrate AI capabilities both into low-code automation flows and high-code stacks, including, applications, APIs, microservices, distributed systems … code and high‐code platforms. Hands‐on use of AI coding assistants (e.g., GitHub Copilot, Claude Code) and autonomous software engineering agents. Exposure to RAG, vector databases, embeddings, and AI retrieval systems. Experience working with cloud AI services and orchestrating AI agents. Exposure to DevOps practices, CI/ ...

repeat manual work. Secure AI Application and Cloud-AI Guardrails Define secure design patterns for AI-enabled applications, including LLM-based features, retrieval-augmented generation, AI agents, AI APIs, copilots, and automation workflows. Establish cloud security requirements for AI workloads, including identity, secrets, data … NIST AI Risk Management Framework, NIST Generative AI Profile, OWASP Top 10 for LLM Applications, or Cloud Security Alliance AI security guidance. Experience with RAG architecture, AI agents, vector databases, AI APIs, prompt engineering, or AI application threat modelling. Experience building security knowledge assistants, prompt libraries, AI-assisted review workflows ...

deliver measurable value. Lead discovery sessions, solution design and technical workshops with senior stakeholders. Design pragmatic AI solutions across Generative AI, Agentic AI, LLMs, RAG and modern cloud platforms. Provide architectural guidance and technical leadership throughout delivery programmes. Remain hands-on where required, supporting engineering teams on complex engagements. Work … before moving into AI. Experience designing enterprise AI solutions using technologies such as: Generative AI Large Language Models (LLMs) Retrieval Augmented Generation (RAG) Agentic AI Cloud AI Services (Azure, AWS or Google Cloud) Comfortable engaging with both technical and non-technical stakeholders. Experience supporting ...

with MLOps concepts and frameworks, such as MLflow, Amazon SageMaker, and feature stores. Familiarity with AI concepts, such as Retrieval‐Augmented Generation (RAG), agentic system architectures, evaluation of LLMs and agents, and Model Context Protocol (MCP). Hands‐on experience with or solid understanding ...

agent system behind our AI doctor: reasoning, planning, tool use, clinical citation, and safe fallback behavior Retrieval & knowledge — vector infrastructure, embedding pipelines, RAG architecture over clinical literature and user health data. You know the difference between retrieval that works and retrieval that a clinician ...

scaleup (roughly 30 to 100 people), not exclusively large corporate environments Hands‐on experience shipping AI or LLM features to production: agentic workflows, RAG, or tool use Strong SQL and solid experience with PostgreSQL or an equivalent relational database A genuine preference for staying hands‐on and coding ...

/ML Lead Engineer in Glasgow to design, build, and productionize Generative AI and Agentic AI solutions. You will deliver GenAI apps using LLMs, RAG, and tool-using agents on AWS and cloud-native platforms. The role emphasizes modern AI engineering workflows, secure coding, observability, and collaboration with cross-functional ...

working closely with engineering, cloud and security teams to define practical controls and secure-by-design patterns. This will include areas such as LLMs, RAG, AI agents, vector databases, prompt injection, data leakage, identity, tool/API integration and AI threat modelling, alongside broader cloud and enterprise security architecture. ...

working closely with engineering, cloud and security teams to define practical controls and secure-by-design patterns. This will include areas such as LLMs, RAG, AI agents, vector databases, prompt injection, data leakage, identity, tool/API integration and AI threat modelling, alongside broader cloud and enterprise security architecture. ...

Product management, AI Strategy and engineering Hands-on experience building and scaling AI or machine learning products in production environments. Experience understanding LLMs RAG Architecture and agentic workflows Excellent experience with Python. Excellent experience working with AI platforms (Claude, Open AI and AI orchestration frameworks) Experience within Financial services. ...

technical BizOps role. Hands-on experience with LLM APIs, agentic frameworks, or workflow automation tools (e.g., Python, Zapier/Make, internal tooling, RAG pipelines). Strong SQL and scripting skills; comfortable moving between spreadsheets, code, and no-code tools as the situation demands. A track record of shipping things that ...

markets and infrastructure Desirable attributes: Hands‐on experience building LLM‐based systems Experience testing non‐deterministic systems Understanding of statistics and inferential methods LangGraph, RAG, MCP, MLOps, IaaC, AWS What we offer Some of the benefits we include are: Private Medical Insurance Dental Insurance Parental Support Salary‐Exchange Pension Employee ...

analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX. Experience with Large Language Models, including RAG and fine-tuning techniques. Familiarity with compute infrastructure necessary to support operating AI and ML technology. For more information about DRW's processing activities ...

frameworks, risk assessments, policies and controls. Practical understanding of how AI systems are built and secured. Good understanding of LLMs and agentic AI, including RAG, tool use and orchestration. Knowledge of AI security risks including prompt injection, data leakage and excessive agency. Strong understanding of relevant AI regulation and frameworks ...