26 to 50 of 501 Retrieval-Augmented Generation Jobs in London

Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG) Familiarity with LLMOps frameworks such as LangChain, LangSmith or LangGraph Understanding of model validation, evaluation techniques and production monitoring Experience working in cross-functional teams from ...

effectively as part of their day-to-day workflow. Bonus points for: Experience with retrieval-augmented generation (RAG) and vector databases (pgvector, Pinecone, or similar).Experience designing multi-agent systems and agent orchestration patterns. Familiarity with LLM observability tools (LangSmith, Langfuse, Arize, or similar ...

deployment of GenAI solutions, including LLM-based applications, prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG) for enterprise use cases. The ideal candidate has a strong foundation in machine learning and NLP, with hands‐on experience in modern GenAI tools and frameworks ...

architecture strategy and roadmap. Build scalable AI/ML and Agentic AI platforms spanning LLMs, retrieval-augmented generation (RAG), embeddings and semantic search. Lead on deployment, governance and integration patterns, advise clients on platform capability and implementation strategy, and partner with engineering, data, DevOps … business teams to deliver end-to-end solutions.**Tech environment**Python, LLMs (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, MLOps/LLMOps, AWS, Azure, GCP, Databricks, Kubernetes and GPU infrastructure.**What we're looking for**Proven enterprise AI or ML architecture design, strong cloud ...

using Azure AI Foundry, Copilot Studio, Databricks Mosaic AI and MLflow. Building, optimising and evaluating Retrieval-Augmented Generation (RAG) solutions using prompt engineering and embedding models. Designing and deploying AI agents using frameworks including LangChain, LangGraph and AutoGen. Delivering production-ready GenAI applications … Delta Lake, Unity Catalog, DLT and MLflow. Deep knowledge of Large Language Models including GPT, Llama, Claude and Mistral. Experience building and deploying GenAI, RAG and Agentic AI solutions in enterprise environments. Strong programming skills in Python, SQL and/or Scala. Experience with Azure cloud technologies including Azure OpenAI ...

scalable AI solutions. Key responsibilities will include: Leading the technical design and delivery of Generative AI and Machine Learning solutions Designing enterprise-grade LLM, RAG and AI agent architectures Building and integrating conversational, voice-to-text and speech-based AI solutions Developing AI applications using commercial and open-source models … candidates with experience across: Generative AI Large Language Models (LLMs) Machine Learning Deep Learning Retrieval-Augmented Generation (RAG) AI Agents and Agentic AI Multi-agent systems Voice AI Speech-to-text/text-to-speech ElevenLabs AWS Bedrock Amazon SageMaker Google Cloud Platform ...

using frontier foundation models (eg Claude, GPT, Gemini family) across use cases including agentic automation, retrieval-augmented generation (RAG), copilots, and multi-modal applications Lead technical solutioning during pre-sales and discovery, translating client business challenges into scalable AI architecture and delivery roadmaps Design …/ML or generative AI Proven, hands-on architecture experience with frontier large language models and multi-modal models - including prompt engineering, fine-tuning, RAG, and agentic/multi-agent system design Strong grounding in LLMOps/MLOps: evaluation frameworks, observability, cost and latency optimisation, guardrails, and safe deployment ...

unsupervised learning, reinforcement learningand LLMs. Experience with the latest AI engineering approaches such asprompt engineering, retrieval-augmented generation (RAG)and agentic AI. Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews etc). Expertisein data engineering ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents.* Scale existing POCs to production-ready solutions for customer use.* Design and develop FullStack applications for both GenAI and non-GenAI projects. ...

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 ...

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 ...

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 ...

architecture, engineering, scalability, and deployment. Design and build AI solutions using large language models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI approaches. Remain hands-on with development, contributing to Python code, architecture reviews, and technical problem-solving. Drive best practices across MLOps, testing … development skills with experience across modern machine learning and data science frameworks. Hands-on experience with large language models, generative AI, prompt engineering, and RAG architectures. Experience with Databricks, MLflow, Spark, and cloud-based AI platforms (Azure preferred). Strong understanding of MLOps, software engineering best practices, and production deployment ...

Position Overview We are seeking a Senior Machine Learning Engineer to design, deploy, and optimize our next-generation Conversational AI and Data Analytics platforms. You will bridge the gap between AI research and production engineering. Your primary focus will be optimizing and scaling core Speech … e.g., Kubeflow, MLflow). Preferred Qualifications Conversational Context: Experience with dialogue management, prompt engineering, and Retrieval-Augmented Generation (RAG). Data Analytics: Familiarity with real-time data streaming (e.g., Kafka) and vector databases (e.g., Pinecone, Milvus, Qdrant). Quantization: Experience with model compression techniques ...

Robert Walters is exclusively partnering with Connect Managed Services to recruit a Machine Learning Engineer to help design, deploy and scale the next generation of Conversational AI and data analytics platforms. This is a hands-on engineering position sitting at the intersection of Machine Learning, Generative … following areas would be advantageous: Conversational AI and dialogue management. Prompt engineering and Retrieval-Augmented Generation (RAG) . Real-time data streaming platforms such as Kafka . Vector databases including Pinecone, Milvus or Qdrant . Model compression and optimisation techniques including INT8/ ...

Deep, hands-on expertise in large language models including model selection, fine-tuning, prompt engineering, retrieval-augmented generation (RAG), vector database design, and evaluation methodologies. You understand how models behave in production, not just in demos.* Agentic Systems Design:Proven experience designing and deploying … grade agentic AI systems** across priority COO domains, with measurable operational impact (cost, speed, quality, or risk reduction)* **Launch the shared AI platform** — reusable RAG infrastructure, evaluation frameworks, and common tooling adopted across COO Technology* **Define and align the multi-year AI roadmap** with COO function leads and the Head ...

Knowledge of MLOps, DevSecOps, CI/CD, and Infrastructure as Code Experience with vector databases, Retrieval-Augmented Generation (RAG), and AI agent frameworks Exposure to GDS standards and government digital delivery practices Contract Details Role: Lead AI Engineer Rate: £560 per day IR35: Inside ...

including role definition, tool integration, planning, memory, and workflow orchestration using modern agent frameworks. Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search with vector databases, including indexing, retrieval policies, and evaluation. Build and integrate agent tools and APIs to connect agents ...

unsupervised learning, reinforcement learningand LLMs. Experience with the latest AI engineering approaches such asprompt engineering, retrieval-augmented generation (RAG)and agentic AI. Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews etc). Expertisein data engineering ...

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 ...

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 ...

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 ...

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 ...

ClinicalKey AI, PharmaPendium, and next-generation life sciences platforms. These products leverage retrieval-augmented generation (RAG), semantic search, and generative AI to make knowledge more discoverable, connected, and actionable across disciplines. The Search & AI Evaluation team sits within the Platform Data Science … clickstream data, user behavior analytics, and event tracking systems Experience supporting evaluation of ML-based ranking or retrieval systems Familiarity with RAG systems or GenAI applications Why join us? Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance ...

frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent; Experience with vector databases and retrieval-augmented generation (RAG) patterns; Experience deploying and scaling LLM-powered applications using APIs such as OpenAI or Anthropic, or open-source models; Strong Python skills; Excellent communication skills ...