51 to 75 of 501 Retrieval-Augmented Generation Jobs in London

Lineage, Master Data Management, and enterprise data catalogues. Strong knowledge of Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), LLM integration, Vector Databases, and AI governance frameworks. Significant experience leading technical pre-sales engagements and developing complex solution architectures. Demonstrable experience responding to large ...

learning operations practices Strong working knowledge of large language models (LLMs), including prompting, fine-tuning, retrieval-augmented generation (RAG), and evaluation techniques, and the ability to apply them to real product challenges Experience with agentic AI frameworks and libraries in Python, such ...

learning operations practices Strong working knowledge of large language models (LLMs), including prompting, fine-tuning, retrieval-augmented generation (RAG), and evaluation techniques, and the ability to apply them to real product challenges Experience with agentic AI frameworks and libraries in Python, such ...

learning operations practices Strong working knowledge of large language models (LLMs), including prompting, fine-tuning, retrieval-augmented generation (RAG), and evaluation techniques, and the ability to apply them to real product challenges Experience with agentic AI frameworks and libraries in Python, such ...

cross-functional organizations.Deep knowledge of modern AI technologies including large language models (LLMs), agentic frameworks, retrieval-augmented generation (RAG), vector databases, and automation platforms — with the credibility to evaluate vendor claims and guide architectural decisions.Experience owning AI governance: building acceptable use policies, risk tiering ...

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 ...

agent frameworks (Pydantic, LangChain, custom orchestration). Data pipelines, ETL, embeddings, or vector search Retrieval-augmented generation (RAG) or model-backed workflows High-performance APIs and distributed services Strong understanding of modern cloud environments (AWS preferred). Solid foundation in computer science, distributed systems ...

/functions callable by AI agents (tool schemas, function calling, structured outputs). Experience with retrieval-augmented generation (RAG) : vector databases (e.g., Pinecone, Weaviate, pgvector), embeddings, and chunking strategies. Working knowledge of prompt engineering and evaluation techniques for agent reliability, safety, and cost control. ...

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 ...

both customers and organisations. Modern AI architecture expertise. Deep understanding of machine learning, LLMs, retrieval-augmented generation (RAG), agentic systems and the architectural patterns used to deploy AI solutions at enterprise scale. Engineering and cloud foundations. Strong understanding of software engineering, data architecture ...

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 ...

generative AI solutions for scientific and knowledge-discovery applications. Develop LLM-powered workflows and retrieval-augmented generation (RAG) systems for search, summarization, question answering, and evidence-grounded insight generation. Build intelligent retrieval, ranking, recommendation, and decision-support capabilities using modern orchestration frameworks … skills and solid machine learning fundamentals. Experience working with large-scale text or content-rich datasets and modern AI/ML frameworks. Experience with RAG, semantic, vector, or hybrid search, along with experimentation and evaluation approaches that measure user impact. Familiarity with cloud platforms and modern software engineering practices. Strong ...

enterprise production environments. Design scalable enterprise AI architectures utilizing Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), AI Agents, Model Context Protocol (MCP), model fine-tuning, inference optimization, vector databases, API integration, AI workflow orchestration, and cloud-native AI services. Collaborate closely … platforms, cloud computing, software engineering, MLOps, solution architecture, or customer-facing technical consulting. Hands-on experience with modern AI technologies including LLMs, Prompt Engineering, RAG, AI Agents, MCP, model fine-tuning, inference optimization, vector databases, AI orchestration frameworks, API integration, and cloud-native AI architectures. Experience developing or deploying enterprise ...

reference architectures, POV frameworks, or architectural playbooks — at scale.Fluency across the modern data and AI stack: cloud data platforms (Snowflake, Databricks, BigQuery), agentic frameworks, RAG, vector databases, LLM orchestration, data governance, and enterprise integration patterns.Experience with prompt engineering, agent lifecycle management, and hands-on prototype development.Hands-on proficiency in programming … Redshift.AI & Agentic Architecture Foundational ML concepts, data preparation, Python, applied AI, Generative AI, prompt engineering, retrieval-augmented generation (RAG), vector stores, and agentic architectures. Hands-on familiarity with platforms like Databricks, Snowflake, SageMaker, or Vertex. Agent lifecycle management, observability, and governance frameworks.Agentforce & Salesforce Platform ...

AWS. Translate business problems into secure, scalable, production-grade AI systems using Amazon Bedrock, retrieval-augmented generation (RAG), agentic workflows, and cloud-native AWS services. Drive architecture standards, model orchestration, governance, observability, and operational excellence across the GenAI lifecycle while collaborating with engineering, security … CloudWatch* Knowledge of scalable, secure, event-driven, and distributed system architectures* Experience integrating structured and unstructured enterprise data sources* Experience with RAG, fine-tuning, model evaluation, data privacy, ethical AI, and cross-functional stakeholder management is expected* Strong understanding of enterprise security architecture, IAM, encryption, and secure AI deployment practices ...

generative AI engineering fundamentals, including: o Prompt engineering and context design. o Context engineering, retrieval-augmented generation (RAG), embeddings and vector search. o Agent orchestration, multi-agent patterns and skills/plugin development. * Experience with cloud-based AI, data, identity and governance services, ideally ...

systems, or conversational AI.Ideally, you also have experience with large language models (LLMs), foundation models, retrieval-augmented generation (RAG), fine-tuning, prompt engineering, or agentic AI architectures. Hands-on experience with AWS AI/ML services such as Amazon Bedrock, Amazon SageMaker, Amazon ...

conversational AI. Ideally, you also have experience with large language models (LLMs), foundation models, retrieval-augmented generation (RAG), fine-tuning, prompt engineering, or agentic AI architectures. Hands-on experience with AWS AI/ML services such as Amazon Bedrock, Amazon SageMaker, Amazon ...

cases for enterprises. Hands-on experience with modern AI/LLM ecosystems, including Retrieval-Augmented Generation (RAG), and familiarity with open-source frameworks such as LangChain, Hugging Face, or PyTorch, along with a solid understanding of how to work with and deploy Large Language ...

enterprise performance, security, and scalability.Agentic AI Implementation: Direct the practical implementation of LLM orchestration, Retrieval-Augmented Generation (RAG), and "Human-in-the-Loop" (HITL) workflows on live customer projects.Standardization & Playbooks: Package learnings, custom connectors, and architectural patterns from active projects into reusable delivery playbooks … platform, including advanced integration patterns, core architecture design, and performance tuning.Agentic AI Architecture: Practical knowledge of multi-agent orchestration, strategic prompt engineering, vector databases (RAG), and establishing token cost and data privacy guardrails.Estimation & Delivery Methodologies: Strong background in Agile/Scrum delivery, defining MVP boundaries, and creating realistic work-breakdown ...

Familiarity with CI/CD pipelines and software development lifecycles Experience with prompt engineering and retrieval-augmented generation (RAG) based architecture Ability to communicate with clarity and credibility across senior engineers, stakeholders, and product partners Preferred Qualifications, Capabilities, and Skills Experience building agentic tools ...

expand agent capabilities, improve decision quality, and support complex business workflows. Design and optimize Retrieval-Augmented Generation (RAG) architectures , including embedding strategies, vector databases, retrieval pipelines, reranking, and context management to maximize response accuracy and relevance. Design and develop APIs, tools ...

Familiarity with CI/CD pipelines and software development lifecycles Experience with prompt engineering and retrieval-augmented generation (RAG) based architecture Ability to communicate with clarity and credibility across senior engineers, stakeholders, and product partners Preferred Qualifications, Capabilities, and Skills Experience building agentic tools ...

AI. Experience developing AI-enhanced digital assistant solutions such as Microsoft Copilot. Practical experience with Retrieval-Augmented Generation (RAG) architectures and Machine Learning Operations (MLOps). Strong stakeholder engagement skills, with the ability to communicate technical solutions clearly to both technical and non-technical ...

electricity markets, battery optimisation and project revenues. Explore the use of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI copilots and autonomous agents to improve knowledge management and engineering productivity. Create scalable data pipelines that ingest, clean and integrate large datasets from weather ...