26 to 50 of 236 Retrieval-Augmented Generation Jobs in London

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

world impact. This position sits within a growing AI function and will focus heavily on Retrieval-Augmented Generation (RAG), agentic workflows, evaluation frameworks, and LLM optimisation. Key Responsibilities: Design and develop scalable RAG pipelines and generative AI workflows Build solutions using LangChain, LangGraph … contribute to technical best practices Key Requirements: Strong Python engineering experience Commercial experience building production-grade AI/ML systems Hands-on experience with RAG architectures and LLM workflows Experience with LangChain, LangGraph, and/or Llama Index Understanding of evaluation methodologies for generative AI Experience working within scalable cloud ...

agentic architectures. Good understanding of modern AI concepts and practices, including: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Prompt Engineering AI Guardrails AI Evaluation Frameworks Experience with AI frameworks such as Agent Development Kit (ADK), LangChain, LangGraph, CrewAI, or similar. Experience working with ...

will combine deep software engineering experience, excellent judgment and a demonstrable interest in AI to help shape the way we build our next generation of products. Initially working in a small, focused team, you will help lead and progress the development of our foundational AI-native/… principles (REST, SPA...). Microservice architecture, AWS and DevOps. Building LLM-powered features: tool-calling, retrieval-augmented generation (RAG), evaluation/eval harnesses and observability (desirable). Familiarity with model-agnostic tooling and routing, e.g. a proxy/gateway layer (desirable). Agile manifesto ...

enterprise environment. Driving build versus buy evaluations and recommending appropriate technologies. Delivering solutions including retrieval-augmented generation (RAG), agentic AI, automation, and intelligent decision-support tools. Working closely with engineering, security, governance, and business stakeholders. Navigating complex and ambiguous problem spaces to identify practical … fundamentals and experience building scalable applications. Experience making architecture and technology decisions within enterprise organisations. Strong knowledge of modern AI engineering practices, including LLMs, RAG, agentic workflows, and MLOps concepts. Experience working with cloud platforms, ideally AWS. Ability to balance innovation with risk, governance, and operational requirements. Strong stakeholder management ...

solution approaches across the modern AI stack, including Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), Vector Databases and MLOps. Support clients in navigating cloud, security, data governance and operating model considerations required for enterprise AI adoption. Communicate complex machine learning ...

modern AI technologies and concepts, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases AI Solution Architecture MLOps Principles AI Governance and Risk Considerations Candidates are not expected to be hands-on developers but should be capable ...

technology teams to optimise semantic models for AI readiness. Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection. Contribute to the organisation's long-term Data & AI strategy, influencing senior stakeholders and executive leadership. Establish best practices, standards, and governance … shape direction and define solutions. Desirable Experience Microsoft Copilot, Azure OpenAI, or similar AI platforms. Retrieval-Augmented Generation (RAG) and semantic grounding techniques. Executive-level analytics solutions and trusted AI implementations. Highly regulated or data-governed environments. What We Offer Opportunity to influence enterprise ...

understanding of the modern AI ecosystem, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases MLOps Experience leading the implementation of enterprise-scale AI solutions and being accountable for delivery outcomes. Knowledge of cloud platforms such ...

implementing AI agents, automation workflows and AI-powered business solutions. Strong understanding of prompt engineering, Retrieval-Augmented Generation (RAG), tool/function calling and LLM integrations. Excellent business analysis, stakeholder management and workshop facilitation skills. Ability to translate complex business requirements into practical, scalable ...

Apply advanced techniques including optimisation, forecasting, statistical modelling and AI Develop LLM-based solutions, including: Retrieval-Augmented Generation (RAG) Fine-tuning and prompt engineering Agent-based workflows Work with large and complex datasets, both structured and unstructured Write clean, scalable Python code for model … Pandas, NumPy, SciPy or similar) Solid grounding in statistics, probability and data analysis AI & LLM Experience Hands-on experience with large language models, including: RAG pipelines Fine-tuning approaches Agent-based architectures A practical understanding of how to apply LLMs to real-world use cases Tools & Engineering Experience with model ...

Guide architecture across teams, helping reduce complexity and establish common patterns. Partner with AI and Retrieval teams to build scalable search and RAG capabilities. Lead complex cross-team technical initiatives from design through production. Improve platform reliability, scalability, observability, and developer experience. Contribute hands-on to critical technical … with cloud infrastructure (AWS), Kubernetes, APIs, and modern backend development. Familiarity with AI-powered systems, Retrieval-Augmented Generation (RAG), search platforms, or vector databases. Strong programming experience in Python or another modern backend language. Comfortable influencing technical direction without direct authority. Excellent communication skills ...

objectives. Nice to have Experience with Microsoft Fabric, Power Platform or Microsoft 365. Exposure to Retrieval-Augmented Generation (RAG), AI agents or AI automation. Microsoft certifications in Azure or AI technologies. Experience working within large enterprise organisations. Contract details 12-month contract ...

resolve failures in real-time. Experience connecting agents to enterprise knowledge bases and optimizing Retrieval-augmented generation (RAG) chunking to prevent hallucinations. Ability to travel up to 50% of the time. Track record of troubleshooting live, high-traffic systems during critical windows. About ...

across the others. Agentic AI & LLM Engineering Design and build production‐grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM‐powered systems; current knowledge of the latest model capabilities ...

clear, evidence‐based recommendations on viability, effort and expected value. Build and maintain a living library of production‐ready patterns covering areas such as RAG, agentic AI, LLM integration, prompt engineering and responsible AI guardrails. Facilitate cross‐domain engineering forums, design reviews, and communities of practice, creating the conditions … agentic systems. Proficiency in Python and modern cloud‐native development practices (Azure preferred). Practical experience with prompt engineering, retrieval‐augmented generation (RAG), fine‐tuning, and AI orchestration frameworks such as LangChain, Semantic Kernel, or equivalent. Strong understanding of data engineering fundamentals ...

/Specialist Expertise Experience building and deploying GenAI solutions using frameworks such as LangChain and Azure OpenAI. Development of enterprise-grade RAG (Retrieval-Augmented Generation) systems, including context engineering and multimodal data pipelines. Design and deployment of autonomous multi-agent systems using modern orchestration ...

delivered across the organization. Key Responsibilities: Lead the design and build of end-to-end generative AI applications using LangGraph, including agentic workflows, RAG pipelines, and LLM-integrated services. Define and drive architecture for scalable, secure, reliable, and maintainable AI solutions deployed in production. Develop robust, well-tested backend APIs ...

equivalents. Ability to apply AI agents across all phases of delivery: requirements, architecture, coding, testing, documentation, and deployment. Experience with LLMs, prompt engineering, RAG, vector databases, and orchestration patterns. Strong Azure expertise preferred, with hands‐on exposure to AWS and GCP. Experience integrating AI into distributed systems, microservices, APIs ...

from idea to production. Strong knowledge of deep learning, NLP, and LLM architectures and experience in fine-tuning LLMs or training domain-specific models, RAG, embeddings, and vector databases. Familiarity with agentic AI concepts and orchestration frameworks. Experience deploying AI solutions on cloud platforms such as Azure, AWS, or GCP. ...

data platforms (Snowflake, Databricks, BigQuery, Azure Synapse) and real-time streaming (Kafka, Flink, Spark Streaming). Familiarity with generative AI patterns (LLM fine-tuning, RAG, prompt engineering, function calling, agent frameworks) and graph/knowledge graph technologies. DevOps/SRE practices at scale: CI/CD, IaC (Terraform, Pulumi), observability ...

deploy AI‐powered applications including chatbots, copilots, document intelligence, and decision‐support tools for insurance use cases.* Implement Retrieval‐Augmented Generation (RAG) patterns using structured and unstructured insurance data (policies, submissions, bordereaux, claims documents).* Build LLM‐based workflows and agents using platforms such … experience with ChatGPT/OpenAI APIs and/or Anthropic Claude.* Practical experience with prompt engineering, system prompts, and evaluation techniques.* Experience implementing RAG architectures, vector databases, and semantic search.**Cloud & Platforms*** Strong experience with Google Cloud Platform (GCP), ideally Vertex AI.* Experience deploying services using Docker/containerised workloads. ...

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 ...

implementing end-to-end AI/ML solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure ...

cloud‐native delivery patterns What Will Make You Stand Out Experience building, deploying, and evaluating multi‐agent AI solutions, agent orchestration patterns, RAG architectures, and enterprise integrations Knowledge of agent communication protocols and interoperability patterns, such as MCP and A2A Ability to balance innovation with practical delivery considerations, including risk ...