76 to 100 of 111 Retrieval-Augmented Generation Jobs in London

similar engineering languages is a must Experience working with APIs, cloud infrastructure and data processing. Previous experience within AI tech such as LLMs, RAG, embedding, vector databases is a big plus as is any previous experience within an AI, Data or B2B SaaS company. This is a great time ...

solutions spanning data preparation, modelling, APIs, and production integration* Build and optimise ML and LLM-driven services, including prediction models, personalisation, and retrieval-augmented systems* Collaborate with business stakeholders to frame problems, define success metrics, and deliver measurable outcomes* Develop services, APIs, and integrations that embed … solutions into production environments* Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn* Experience working with LLMs, including prompt design, RAG, or fine-tuning* Experience building APIs and integrating AI systems into real products* Solid understanding of data pipelines, feature engineering, and model evaluation* Experience working ...

What You’ll Work On Designing and implementing Agentic AI architectures for enterprise workflows. Building and deploying LLM-powered solutions, including Q&A systems, RAG pipelines, and autonomous agents. Developing custom copilots, plugins, and declarative agents using Microsoft Copilot Studio and Azure AI Agent Service. Operationalising AI solutions using Microsoft … Python and deep learning/NLP libraries (LangChain, HuggingFace, PyTorch, TensorFlow). Experience deploying AI workloads on Azure (ACA, AKS, Azure Functions) and implementing RAG with Azure AI Search. Ability to engage with clients, translate business needs, and deliver scalable end-to-end AI solutions. Why Join: Work directly with ...

want to build production GenAI products (not prototypes) in a business with real scale and board-level visibility? Have you shipped LLM/RAG systems end-to-end and owned them in production? Are you ready to join a new AI team early and shape how AI is delivered across … services that directly improve customer experience and business performance. Key responsibilities Design, build and deploy end-to-end AI solutions Develop LLM solutions (e.g. RAG, workflow orchestration, evaluation) Build APIs/services integrating data sources and AI outputs Translate stakeholder needs into practical technical delivery plans Own delivery quality: reliability ...

want to build production GenAI products (not prototypes) in a business with real scale and board-level visibility? Have you shipped LLM/RAG systems end-to-end and owned them in production? Are you ready to join a new AI team early and shape how AI is delivered across … services that directly improve customer experience and business performance. Key responsibilities Design, build and deploy end-to-end AI solutions Develop LLM solutions (e.g. RAG, workflow orchestration, evaluation) Build APIs/services integrating data sources and AI outputs Translate stakeholder needs into practical technical delivery plans Own delivery quality: reliability ...

office The Role AWS Solution Designer (with GenAI experience) Your responsibilities: Lead end to end GenAI initiatives including model creation, finetuning, benchmarking, evaluation, and RAG system design click apply for full job details ...

line management and team leadership within a technical environment. Expertise in AI and Machine Learning, including agent-based AI, RAG (Retrieval Augmented Generation), multi-agent systems and conversational agents. Strong programming skills in Python, Java or .NET. Experience with GenAI and LLMs (e.g. ChatGPT ...

developing machine learning models across forecasting, classification, and anomaly detection use cases. Building and deploying GenAI and LLM-enabled solutions, including prompt-driven and RAG-based architectures. Running structured experimentation cycles, defining hypotheses, designing experiments, and evaluating results within governance constraints. Transitioning validated experiments into production-ready systems in collaboration … tooling such as MLflow, Weights & Biases, or Data Version Control. Experience developing or integrating LLM and GenAI applications, including prompt engineering and retrieval-augmented generation. Ability to work across disciplines and communicate technical findings clearly. A pragmatic mindset and good judgement about when machine learning ...

translate business outcomes into actionable AI roadmaps and solution designs. You will also guide implementation teams on best practices for prompt engineering, grounding strategies, RAG patterns, observability, MLOps, and Responsible AI-ensuring solutions are robust, compliant, and production ready. This role suits a hands on architect who can balance strategic … Foundry, Azure OpenAI, and Azure AI Document Intelligence for enterprise use cases (IDP, GenAI copilots, knowledge retrieval). Establish standards for RAG architectures (vectorization, indexing, chunking, grounding, citation policies) using Azure Search or vector enabled data stores. Create architectural decision records (ADRs), guardrails, and reusable blueprints for solution ...

Agentic AI solutions aligned to real business workflows Build and orchestrate autonomous agents with planning, reasoning, and tool use Develop LLM-driven systems including RAG, Q&A, and enterprise copilots Integrate GenAI into existing platforms, products, and processes Own delivery end-to-end: design through to production Work closely with … Generative AI and LLMs Agentic AI frameworks such as LangGraph, AutoGen, or CrewAI Python with modern AI libraries (LangChain, Hugging Face, PyTorch) Experience building RAG pipelines and knowledge retrieval systems Cloud platforms: Azure, AWS, or GCP Microsoft/Azure (Highly Desirable) Microsoft AI Foundry Azure AI Agent Service ...

using Google's ADK. Integrate AI systems with policy, claims and CRM platforms through secure REST and JSON API services. Develop scalable RAG pipelines including ingestion, text processing, metadata handling and vector search. Implement observability and evaluation frameworks covering monitoring, A/B testing, accuracy and safety metrics, and cost … Cloud Functions, Model Garden and ADK. Strong Python skills for backend development, APIs and data processing. Hands-on experience with LLMs, embeddings, RAG design, vector databases and retrieval pipelines. Experience deploying production AI with robust evaluation, monitoring, observability and automated testing. Solid exposure to DevOps tooling including ...

Foundry , Azure OpenAI , and Azure AI Document Intelligence for enterprise use cases (IDP, GenAI copilots, knowledge retrieval). Establish standards for RAG architectures (vectorization, indexing, chunking, grounding, citation policies) using Azure Search or vector-enabled data stores. Create architectural decision records (ADRs), guardrails, and reusable blueprints for solution … Analytics, Synapse Analytics, Azure DevOps . You should have hands-on experience in Deep knowledge of LLMs (prompting, system prompts, grounding, evaluation), embeddings , and RAG design (index selection, chunking strategies, reranking, citations). Strong design of secure API and event-driven integration patterns; familiarity with microservices and domain-driven design ...

RAG & Agentic Consulting Engineer Insurance Sector Remote-first Occasional travel client offices and two trips to London HQ per month Role Overview Lead the design and delivery of AI-native transformation initiatives for insurance clients, spanning agentic systems, retrieval architectures, semantic layers and decision intelligence. This … consulting engineering role. Candidates should bring: Experience in software engineering, AI engineering or applied data engineering Strong hands-on experience with LLMs, embeddings, RAG pipelines and vector databases Experience designing or implementing multi-agent systems or tool-calling frameworks Strong Python skills with experience building production-grade, regulated systems Experience ...

Engineer to design, build, and deploy production-ready Generative AI systems. Role You will own the full lifecycle of GenAI applications, working with LLMs, RAG architectures, and agentic frameworks to deliver scalable, business-focused solutions. Responsibilities Build and deploy GenAI and RAG systems Develop agentic AI workflows Design prompts … with clients and technical teams Requirements 1+ year deploying LLM/GenAI systems in production Strong Python skills Experience with cloud platforms Knowledge of RAG, vector databases, and LLM frameworks Git and CI/CD experience Experience with LangChain and LangGraph AWS experience IT/Technology-related university degree (minimum ...

that enhance global sales workflows. Build scalable backend services and full-stack applications for internal teams. Architect, implement, and optimize LLM orchestration frameworks and RAG-based solutions. Collaborate with cross-functional teams to define requirements and deliver innovative tools. Ensure reliability, scalability, and performance of distributed systems handling massive datasets. … workflows . Strong proficiency in Angular (primary frontend); React optional. Hands-on experience with LangChain, LangGraph, and Indexify , with a deep understanding of RAG architectures . Expert-level SQL and experience with BigQuery and vector databases (Pinecone, Weaviate, Vertex AI Search). Proven track record in MLOps and deploying ...

that enhance global sales workflows. Build scalable backend services and full-stack applications for internal teams. Architect, implement, and optimize LLM orchestration frameworks and RAG-based solutions. Collaborate with cross-functional teams to define requirements and deliver innovative tools. Ensure reliability, scalability, and performance of distributed systems handling massive datasets. … workflows . Strong proficiency in Angular (primary frontend); React optional. Hands-on experience with LangChain, LangGraph, and Indexify , with a deep understanding of RAG architectures . Expert-level SQL and experience with BigQuery and vector databases (Pinecone, Weaviate, Vertex AI Search). Proven track record in MLOps and deploying ...

engineer with strong software engineering fundamentals? Have you deployed AI models into production—not just trained them? Want to work on agentic systems, RAG pipelines, and LLM-powered tools? We're working with a high-growth GenAI consultancy building real-world, production-grade AI applications. With a team of 50+ … teams and mentoring other engineers. Key Responsibilities Build and deploy scalable AI tools using Python and LLM APIs Design end-to-end agentic and RAG-based solutions for enterprise use Own delivery from proof-of-concept through to production Work with cloud-native architectures (e.g. AWS Lambda, Step Functions) Lead ...

Skills for the Artificial Intelligence Architect: Proven experience architecting solutions across Generative AI, Agentic AI, machine learning and automation Strong understanding of prompt engineering, RAG pipelines, model tuning, MLOps and LLMOps Experience delivering enterprise-scale RAG solutions using LLMs and vector databases Ability to design AI platforms that integrate with ...

want to shape how LLMs are deployed at scale in a real business? Have you built RAG or agentic systems that moved beyond experimentation? Are you motivated by measurable customer and commercial impact, not demos? A private-equity-backed consumer services business is scaling its Data Science & AI capability … year transformation. Following an award-winning data programme, the company now has an established AI function and is investing further in LLM-powered assistants, RAG pipelines, voice/chat intelligence and early agentic workflows across both customer-facing and internal products. This Senior AI Engineer role is a hands ...

products at scale. Sitting at the core of AI delivery, you will design, build, and operate the runtime, infrastructure, and operational layers supporting RAG pipelines, LLM orchestration, vector search, and evaluation workflows across AWS and Databricks. Working closely with senior AI engineers and product teams, you'll ensure AI systems … With further scope of responsibilities detailed below: Own and evolve the AI platform powering conversational assistants and generative AI products. Build, operate, and optimise RAG and LLM-backed services, improving latency, reliability, and cost. Design and run cloud-native AI services across AWS and Databricks, including ingestion and embedding pipelines. ...

months IR35: Outside Location: London (onsite once per month ) Start: ASAP What you’ll work on Building Agentic AI workflows and automation Designing RAG pipelines for insurance data Developing LLM-powered tools using Python , LangChain , LlamaIndex , and vector databases Evaluating, fine-tuning, and integrating LLMs (OpenAI/Anthropic/Mistral … Data Science & Engineering teams to productionise models What you’ll need Strong Data Science + AI Engineering background Hands-on experience with Agentic AI , RAG , LLMs , LangChain , Python Knowledge of vector search, embeddings, and scalable pipelines Experience working in enterprise or regulated environments Must have insurance domain experience Insurance domain ...

Responsibilities • Build AI PoCs and MVPs in fast innovation cycles (2–6 weeks) • Develop GenAI solutions using LLMs, SLMs and vision models • Implement RAG pipelines and agentic workflows • Build lightweight APIs using Flask or Django • Deploy solutions using serverless and cloud-native services • Collaborate in client workshops, demos and innovation … Salary: £70k–£85k base (flexible for strong profiles) • Working model: Hybrid, 3 days per week in London office • Tech stack: Python/JavaScript, GenAI (RAG, LangChain, LlamaIndex), agentic frameworks, Azure/AWS • Visa: No sponsorship available Interested? Please apply below. ...

development mindset , integrating AI tools into the SDLC for code generation, testing, debugging, and optimisation. Design and implement AI-powered capabilities , including RAG workflows, intelligent search, and recommendation systems. Stay ahead of emerging trends in AI and full-stack development , and apply them to real-world solutions. Mentorship … coding practices , data privacy, and compliance requirements. Experience building reliable, production-ready systems at scale. AI Experience Experience working with LLMs, embeddings, LangChain , and RAG-based architectures. Familiarity with AI-driven automation to improve engineering efficiency. Why Join ECA? Work on innovative, AI-powered full-stack solutions Lead and influence ...

depth knowledge in implementing solutions using AWS. Deep understanding of AI/ML solutions implementation, with emphasis on LLM applications and retrieval-augmented generation. Practical cloud-native experience, specifically with AWS (compute, storage, networking, IAM, KMS, observability). Experience with RAG-based semantic search ...

depth knowledge in implementing solutions using AWS. Deep understanding of AI/ML solutions implementation, with emphasis on LLM applications and retrieval-augmented generation. Practical cloud-native experience, specifically with AWS (compute, storage, networking, IAM, KMS, observability). Experience with RAG-based semantic search ...