76 to 100 of 256 Retrieval-Augmented Generation Jobs in London

complex, multi‐region AI programs; define solution strategy, roadmap, and measurable outcomes. Lead workshops and discoveries focused on AI‐enabled engineering (LLMs, agents, RAG, prompt ops, model ops). Architect AI integrations across APIs, pipelines, cloud services, and enterprise toolchains; enforce reliability, safety, and cost discipline. Operationalize agentic engineering (task ...

/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 ...

Generative AI. Demonstrated ability to architect end‐to‐end Data & AI solutions, with specific expertise in modern Generative AI concepts (e.g., fine‐tuning, RAG, MLOps for LLMs), using either open source and/or ISV tools such as Dataiku, Domino, DataRobot, etc. Strong proficiency in a core programming language (e.g. ...

Generative AI. Demonstrated ability to architect end‐to‐end Data & AI solutions, with specific expertise in modern Generative AI concepts (e.g., fine‐tuning, RAG, MLOps for LLMs), using either open‐source and/or ISV tools such as Dataiku, Domino, DataRobot etc. Strong proficiency in a core programming language (e.g. ...

Python and experience with AI/ML libraries. Familiarity with cloud infrastructure (AWS preferred) for deploying AI systems. Understanding of RAG (Retrieval-Augmented Generation), vector databases, and memory systems. Experience with RPA or automation tooling is desirable. Knowledge of responsible AI principles ...

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 ...

technical audiences. Experience gathering and synthesizing requirements across multiple stakeholder groups and influencing product or platform roadmaps. Familiarity with LLM ecosystems (e.g., prompting, RAG, agents, MCP, fine‐tuning, evaluation) and major AI platforms/APIs. Experience in financial services, asset management, or portfolio analytics is a strong plus. Good ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

skills, ideally including FastAPI. Strong AWS experience, including services such as Bedrock, Lambda, ECS/EKS, API Gateway and S3. Experience with LangChain, LangGraph, RAG, vector databases and multi-agent systems. Experience building APIs, platforms, tools and AI-enabled products. Familiarity with evaluation and observability tools such as Arize, Promptfoo ...

preferred) and modern MLOps practices. Commitment to people development: mentoring, performance conversations, and building high‐performing teams. Bonus: experience with NLP on financial contracts, RAG or LLM‐based applications on structured data, large‐scale loan data pipelines, or containerization tooling (Docker, Kubernetes, Terraform). Competitive salary and performance‐based bonus. ...

individual demos.Understanding of Azure security and identity controls as they apply to AI workloads: managed identities, private endpoints, and data residency.Develop and deploy RAG-based knowledge systems using Azure AI Search, Azure OpenAI Service, and relevant orchestration frameworks (LangChain, Semantic Kernel, or Prompt Flow).Design and implement AI agent architectures ...

services, model integration, model monitoring, evaluations, and deployments. Integrate and optimise LLMs for specific use cases in business planning, including prompt engineering and RAG implementation. Build cutting‐edge conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language. Implement evaluation frameworks to measure and improve GenAI ...

production LLM workflows, including prompt/skill design, evaluation, cost control, provider routing, agentic/human‐in‐the‐loop orchestration, embeddings, vector search and RAG Implementation of foundational ML algorithms, classic NLP techniques and LLMs Strong SQL for analytical and production use, ideally PostgreSQL, including complex joins, CTEs and aggregation ...

backend API services, model integration, model monitoring, evaluations and deployments Integrate and optimise LLMs for specific use cases in business planning, including prompt engineering, RAG implementation Build conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language Implement evaluation frameworks to measure and improve GenAI feature ...

based microservices for scalable, high-throughput architectures* Experience in cybersecurity, SIEM, SOAR, or adjacent security domains* Hands-on experience with LLM integration, prompt engineering, RAG pipelines, or agentic AI frameworks#LI-GO1#LI-HYBRID **Benefits of Working at CrowdStrike:*** Market leader in compensation and equity awards* Comprehensive physical and mental wellness programs ...

understand what makes agents perform well in production and how to systematically improve quality through observability and evaluation. Experience with voice AI platforms, RAG systems, and LLM orchestration frameworks is highly desirable. You bring exceptional communication skills, customer empathy, and the drive to build AI solutions that transform enterprise operations … integration Proficiency in Python (production-grade, async programming, type hints) and JavaScript/TypeScript (full-stack development) RAG implementation experience built retrieval-augmented generation systems with vector databases MLOps & deployment hands-on with Docker, Kubernetes, CI/CD pipelines, and infrastructure-as-code Cloud ...

Lead Strategic Discovery: Identify high‐impact AI opportunities by running hands‐on workshops and aligning stakeholders. Architect AI‐Native Solutions: Design systems using LLMs, RAG pipelines, retrieval logic, and workflow orchestration. Accelerate Delivery with Engineers: Collaborate closely with FDSEs to build, test, and iterate functional solutions quickly. Codify … over at least 3+ years. Technical Mastery: Strong grasp of LLM‐based systems and GenAI pipelines including prompt engineering, retrieval‐augmented generation (RAG), orchestration, and data modeling. Experience designing full‐stack intelligent workflows. Cloud & Infrastructure Fluency: Deep hands‐on experience with AWS, Azure ...

ongoing optimisation of token usage and compute cost across AI workloads. Some Other Highly Valued Skills May Include Retrieval Augmented Generation (RAG) and vector database implementation, with practical experience using technologies such as OpenSearch, FAISS or similar to support scalable, production‐ready retrieval ...

ideally TypeScript/React for front end UI) and Java, and comfortable building production systems directly. Deep understanding of LLMs, retrieval‐augmented generation (including hybrid retrieval across semantic, graph and keyword search, with reranking and embedding‐model selection), knowledge bases, knowledge graphs ...

usable, performant ways for brokers and analysts. AI‐Enabled Development: Build AI‐enabled product features using patterns such as retrieval‐augmented generation, tool and API integration, evaluation and human‐in‐the‐loop design. AI‐Assisted Engineering: Use AI‐assisted engineering tools such as Claude ...

5+ years experience within software development and AI related rolesStrong experience building GenAI/Applied AI solutionsHands-on with LLMs, AI Agents, Agentic Workflows & RAG Proficiency in Python (TypeScript beneficial)Full-stack development with React, Next.js or SvelteAPI and backend development, including scalable microservicesExperience with SQL, NoSQL and Vector DatabasesCloud ...

Knowledge of Airflow, Kafka, Flink, dbt, Kubernetes is highly beneficial. Good understanding of AI platforms, experience building infrastructure for LLM orchestration (e.g., vector databases, RAG pipelines, GPU scaling) is a significant plus. #J-18808-Ljbffr ...

Python and JavaScript Hands-on experience with backend frameworks and modern frontend frameworks Deep knowledge of Generative AI and agent-based AI frameworks including RAG and orchestration patterns Strong experience working with vector databases and AI-specific data stores Solid understanding of AI and ML models, LLMs and generative ...

GitHub, Kubernetes, containerisation or equivalent enterprise DevOps platforms. Experience supporting AI, machine learning, data science, analytics or automation teams. Experience with LLM application architecture, RAG, vector stores, orchestration frameworks, model APIs or context management. Experience integrating third‐party or public data APIs into enterprise environments. Experience defining platform operating models ...

scientific, engineering, or R&D workflows encompassing experimentation, simulation, optimisation, physics-informed modelling, and autonomous scientific workflows. Strong experience with generative AI (LLMs, RAG, multimodal systems) and their deployment in production. Experience designing or deploying Agentic AI systems including autonomous agents, tool use, multi-agent orchestration, reasoning workflows, and agent ...