101 to 125 of 500 Retrieval-Augmented Generation Jobs in London

high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype … pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype … pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype … pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

founders with a track record of successful exits. With teams across the US, UK, and India, we’re building the next generation of enterprise AI and we’re looking for exceptional people to help us scale. Who You Are You're a customer‐focused AI consultant and software … designing and delivering enterprise software, cloud, or AI solutions for customers. Strong understanding of Large Language Models, Generative AI, Retrieval‐Augmented Generation (RAG), AI agents, and enterprise AI architectures. Experience with cloud platforms including AWS, Azure, or Google Cloud Platform. Understanding of APIs, microservices ...

technical direction credibly and make sound architecture decisions. Demonstrated experience with AI/ML technologies, including LLMs, Retrieval Augmented Generation (RAG), agentic workflows, Natural Language Processing (NLP), and predictive analytics, and the discipline to run them reliably in production. Track record of building … Across TR: Legal, Data & Analytics, CTO, Product, CIO and wider CDO organisations, and Internal Audit. Technologies You’ll Work With LLMs and agentic frameworks RAG architectures AI agents and orchestration (e.g. LangChain/LangGraph) Python and modern data engineering Snowflake and governed semantic layers AWS/GCP cloud services Vector ...

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 ...

associated data science ecosystem (Pandas, NumPy, Scikit-learn).* Machine learning techniques including classification, regression, clustering and anomaly detection.* Experience with Generative AI, LLMs, RAG architectures or agent-based AI solutions.* SQL and large-scale data analysis.* Cloud-based data science platforms and MLOps practices.* Data visualisation and storytelling tools.### ...

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 ...

including data structures, algorithms, and software design patterns.*** **Agentic AI: Hands-on experience with AI Agents, Agent Development Kit (ADK), multi-agent frameworks, and RAG integration.*** **MLOps/DevOps: Experience with Kubeflow, Docker, Kubernetes, and Linux environments.*** **CI/CD: Proficiency in building and managing continuous integration and deployment pipelines. ...

with Python and modern AI/ML frameworks such as PyTorch and/or TensorFlow . Deep understanding of LLMs, Generative AI, prompt engineering, RAG architectures and vector databases . Experience with frameworks and tooling such as LangChain, LangGraph or similar open-source AI toolkits . Experience fine-tuning, evaluating ...

including data structures, algorithms, and software design patterns. Agentic AI: Hands-on experience with AI Agents, Agent Development Kit (ADK), multi-agent frameworks, and RAG integration. MLOps/DevOps: Experience with Kubeflow, Docker, Kubernetes, and Linux environments. CI/CD: Proficiency in building and managing continuous integration and deployment pipelines. ...

organisations from identifying opportunities through to enterprise adoption and measurable business impact. Strong understanding of Generative AI, Agentic AI, Retrieval Augmented Generation (RAG), intelligent assistants and AI-enabled automation, with awareness of major enterprise AI ecosystems such as OpenAI, Google, Anthropic and Microsoft. Experience … Strategy Director, Enterprise AI Consultant, AI Consulting Lead, Data & AI Strategy Consultant, Generative AI, GenAI, Agentic AI, AI Agents, RAG, Retrieval Augmented Generation, Decision Intelligence, Intelligent Automation, AI Operating Models, AI Architecture, Data Platforms, Semantic Layers, Data Products, Responsible AI, AI Governance, Cloud Data ...

organisations from identifying opportunities through to enterprise adoption and measurable business impact. Strong understanding of Generative AI, Agentic AI, Retrieval Augmented Generation (RAG), intelligent assistants and AI-enabled automation, with awareness of major enterprise AI ecosystems such as OpenAI, Google, Anthropic and Microsoft. Experience … Strategy Director, Enterprise AI Consultant, AI Consulting Lead, Data & AI Strategy Consultant, Generative AI, GenAI, Agentic AI, AI Agents, RAG, Retrieval Augmented Generation, Decision Intelligence, Intelligent Automation, AI Operating Models, AI Architecture, Data Platforms, Semantic Layers, Data Products, Responsible AI, AI Governance, Cloud Data ...

earlier in the lifecycle. You will also support the adoption of AI/GenAI capabilities in practical QE use cases such as test generation, defect analysis, and quality reporting. What you'll be doing: Design, build, test, and maintain quality engineering solutions across the SDLCContribute to the development … ideally in testing, automation, or developer productivity use cases. This may include experience with: AI application patterns Retrieval-Augmentation Generation (RAG)prompt engineeringtool callingagentic workflowsevaluation of LLM-based outputs Frameworks and orchestration LangChainLangGraphCrewAI Models and platforms OpenAI and/or Azure OpenAI Vector search and retrieval ...

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 ...

/inference optimization, MLOps lifecycle management, and integration with cloud‐native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool‐calling, multi‐agent orchestration, and guardrails), AI observability, and natural language querying of structured data. Provide advanced technical support ...

/inference optimization, MLOps lifecycle management, and integration with cloud-native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool-calling, multi-agent orchestration, and guardrails), AI observability, and natural language querying of structured data. Provide advanced technical support ...

Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists J.P. Morgan is a global leader in financial services, providing strategic advice ...

experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems Experience designing and scaling low-latency, customer-facing ...

productivity, service improvement or risk reduction. The role will design and deliver pragmatic AI-enabled workflow improvements, including generative AI, agentic workflows, retrieval-augmented generation, process automation, analytics and decision-support solutions where appropriate. The role will act as the engineering bridge between … policy and control constraints. Preferred: Enterprise GenAI – Experience implementing generative AI or agentic AI solutions in enterprise environments. Knowledge workflows – Experience with retrieval-augmented generation, vector search, knowledge management, document intelligence or workflow orchestration. Regulated industry – Experience in banking, capital markets, financial services ...

high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype … party services via MCP and related protocols, and define the patterns other engineers build against Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Build and deliver compelling proofs of concept, live ...

Generative AI & LLM Engineering: 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 … 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 ...

practical experience. Machine Learning: Expertise in time‐series forecasting, clustering, and correlation algorithms. Generative AI: Experience with LLM prompting, Retrieval‐Augmented Generation (RAG), and LangChain. Cloud Infrastructure: Deep understanding of AWS, Azure, or GCP infrastructure components Additional Preferred Qualifications: Experience with enterprise observability ...

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 ...

advanced degree preferred. Agentic AI/LLMs: LLM orchestration, prompt engineering, context‐window management and production guardrails; MCP, tool‐augmented agents and RAG at enterprise scale. Enterprise integration: Integrating AI into enterprise systems via APIs and data pipelines, with strong security, data governance and residency practices. Cloud ...