26 to 50 of 102 Retrieval-Augmented Generation Jobs in the City of London

Python and familiarity with deep learning/NLP libraries (LangChain, PyTorch, TensorFlow, HuggingFace Transformers). Experience with building Q&A systems and retrieval-augmented generation pipelines. Knowledge of vector databases or semantic search concepts. Familiarity with cloud AI platforms (AWS Bedrock, Azure OpenAI … deploying AI workloads to Azure Container Apps (ACA), Azure Kubernetes Service (AKS), or serverless functions (Azure Functions) for event-driven agent triggers. Experience implementing RAG using Azure AI Search (vector, semantic, and hybrid search) and OneLake/Microsoft Fabric. Nice to Have Skills Certification: Microsoft Certified: Azure AI Engineer Associate ...

/Vertex AI , including fine-tuning, vector search, and low-latency inference. Build end-to-end LLM applications , leveraging RAG (Retrieval-Augmented Generation) , agentic workflows, and prompt engineering. Implement robust evaluation frameworks to monitor LLM quality, hallucinations, token usage, and content safety. Develop … Looking For Essential 5+ years' experience in machine learning engineering or applied AI roles. Recent, demonstrable experience with LLMs, Generative AI, and/or RAG-based systems . Strong Python skills using frameworks such as PyTorch, TensorFlow, Hugging Face, or Google GenAI . Experience with vector databases and retrieval ...

building GenAI applications using Python Hands-on experience with API development and Microservices architecture and end to end integrations Knowledge of RAG (Retrieval-Augmented Generation ) and ADK, MCP Solid understanding of LLMs, prompt engineering, and graph-based workflows Hands-on experience with API development ...

from experimentation into live systems Experience with MLOps tools (MLflow, Weights & Biases, DVC, or similar) Hands-on exposure to LLM/GenAI applications, including RAG Nice to have: Experience with advanced LLM techniques (e.g. agents, tool use) Familiarity with vector databases and feature stores Experience with Docker, Kubernetes, or Infrastructure ...

analyse and summarise technical content into high-accuracy insights and reports • Deliver production-grade AI agent solutions such as intelligent assistants or insight-generation tools • Collaborate with research, engineering and strategy teams to support technical intelligence workflows • Perform any additional duties reasonably required to support project objectives Required … with agent development frameworks such as LangChain, AutoGen, LlamaIndex , etc • Strong proficiency in Prompt Engineering and Context Engineering • Working experience with RAG (Retrieval-Augmented Generation) • Excellent programming skills (Python preferred) • Solid software engineering background with experience delivering production-quality systems Desired Skills • Experience working ...

Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle (including experimentation, training ...

shape the organisation’s LLM strategy, conversational AI capabilities, and broader applied AI roadmap. Key Responsibilities Design, build, and enhance LLM-driven tools, including RAG pipelines, embeddings, and prompt engineering. Support and expand the organisation’s chatbot capabilities. Work with unscripted data (text, call transcripts) to define key insights ...

Proven ability to transition validated models into production-ready systems with appropriate governance Exposure to LLM/Generative AI applications, including prompt engineering and RAG architectures Ability to communicate complex technical concepts clearly to non-technical stakeholders Experience working in cross-functional teams to deliver production solutions Eligibility and willingness ...

Services industry. Experience with Generative AI, Large Language Models (LLMs), and advanced frameworks like: LangChain, LangGraph, Haystack LlamaIndex, CrewAI, AutoGen, Transformers (Hugging Face) and RAG pipelines. Proven experience in leading teams and managing complex AI projects. Expertise in machine learning, data modelling, and advanced statistical techniques. Strong programming skills ...

Architect & Engineer AI Systems: You won’t just call an API; you will build robust processing architectures that leverage off-the-shelf LLMs, RAG pipelines, and context engineering to solve hard problems. You will benchmark these systems rigorously to ensure they actually work, not just "feel" smart. Build the Backend ...

models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong ...

Permanent Required Experience of architecting and solutioning in Gen AI, Agentic AI, classic ML and automation space Experience and good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face ...

Machine Learning , and the wider Microsoft ecosystem. Develop agentic workflows using frameworks such as Autogen, Swarm, or LangGraph. Design and implement RAG (Retrieval-Augmented Generation) systems and integrate them into business processes. Integrate OpenAI GPT models , embeddings, and vector stores into production-grade solutions. … What We’re Looking For Strong experience building AI/LLM solutions on Azure AI , Azure ML, or similar cloud environments. Practical experience implementing RAG pipelines , embeddings, and vector search. Exposure to agent-based architectures and workflow orchestration. Solid Python engineering skills, including familiarity with ML frameworks and LLM toolchains. ...

engineering experience Strong experience with modern GenAI/LLM frameworks: LangChain, LangGraph, PyTorch, etc. Comfort working with unstructured data: entity extraction, semantic search, vectorisation, RAG-like patterns Awareness of data engineering: pipelines, legacy data extraction, interfacing with data teams Experience or strong curiosity working with Azure and Azure Databricks ...

production code practices) alongside practical TypeScript experience for bot or workflow integrations. Hands-on experience with LLM or conversational-AI systems , including model APIs, RAG patterns, or agent frameworks. Solid Kubernetes and Docker skills , with at least one real production service shipped in containers. Good command of SQL and data ...

Experience delivering customer-facing products and supporting the full development lifecycle. Excellent communication, stakeholder engagement, and advisory skills. Desirable Experience with Generative AI, LLMs, RAG, LangChain, or Semantic Kernel. Familiarity with Microsoft Power Platform. Front-end experience with React, Angular, Vue.js, Flutter, or Progressive Web Apps. Exposure to edge computing ...

high-impact projects while building and leading a team from the ground up. Key Skills/Experience: Experience with LLMs, generative AI models, and RAG pipelines. Frameworks: OpenAI, Anthropic Claude, Gemini, and open-source models Agentic AI, Langgraph, Crew AI, Autogen Python Development (Hands-on coding) Must have deployed ...

tools, and enterprise connectors. Skills: 6–8 years in Software/application engineering, AI engineering, or applied data engineering. Strong experience with LLMs, embeddings, RAG, retrieval stacks, and vector stores. Hands-on experience in multi-agent systems, agent orchestration, or MCP-like tool patterns. Proficiency in Python, including ...

Bicep/Terraform is a plus). Agentic AI & LLMs: practical delivery of agents (eg, task planning, tool calling) and LLM apps (chat, summarisation, RAG, structured outputs). Engineering excellence: CI/CD, automated testing, code reviews, observability, and secure development practices. Nice to have Experience with Azure OpenAI ...

company, roles range from founding engineer positions to senior hires in fast-growing teams. What you’ll work on LLM features in production (RAG, evals, document workflows, agent-style automation) API-first platforms and data-heavy systems that power AI products End-to-end delivery: architecture, build, deploy, iterate with ...

autonomous AI What You’ll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical ...

sense of ambiguous problem statements. Our client problems can range in a variety of domains. Some examples, but not limited to, are building a RAG leveraging open source or commercial LLMs, building a Multiagent system, leveraging Small Language models that could be locally resident, problems in computer vision, speech, Natural ...

solutions with the Microsoft Power Platform Familiarity with Azure or AWS A strong interest in AI/LLMs. Practical experience with Dify, or building RAG applications is a major plus. ...

/experience/knowledge: Experience in architecting and solutioning in Gen AI, Agentic AI, classic ML, and automation space. Good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability. Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face ...

systems: From prototypes to scaled deployments, you’ll design and ship systems that power real-world experiences: personalised journeys, churn prediction, lead scoring, RAG-based knowledge assistants, energy optimisation, operational AI tools, and more. Data, modelling & experimentation: Analyse and prepare data for AI use-cases: consumer behaviour, CRM interactions, digital … techniques, including: Classical Machine Learning, e.g. regression, tree-based methods, uplift models, time-series forecasting, LLM and generative AI, e.g. prompt engineering, retrieval-augmented generation, fine-tuning or adapters where appropriate. Design and implement prediction models for use cases like churn, propensity, next best ...