101 to 125 of 252 Retrieval-Augmented Generation Jobs in England

Azure and AWS, with experience supporting and maintaining cloud‐based infrastructure. Hands‐on experience working with LLM‐based solutions, including Retrieval‐Augmented Generation techniques and prompt engineering approaches. Hands‐on experience working with data processing frameworks and platforms, including batch and streaming pipelines. Practical ...

/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 ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...

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 ...

standards at global scale, including model optimisation, quantisation, and efficient serving strategies Drive model customisation and adaptation strategies (prompt engineering, retrieval‐augmented generation, parameter‐efficient and full fine‐tuning) to deliver differentiated product experiences Build end‐to‐end AI‐powered products and features, taking ...

governance position: you make the architectural calls rather than facilitate them. You will: Design large-scale agentic AI architectures: LLM document processing, structured extraction, RAG, invoice-to-contract matching Own low-level design decisions, including API design, data models and service boundaries Set and drive technical processes: engineering standards, security ...

/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. Product Influence: Collaborate cross‐functionally ...

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. ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

practices within engineering teams Drive improvements in automation frameworks and testing tools Provide expert oversight on AI model validation, including Generative AI and RAG solutions Identify risks early and implement mitigation strategies Mentor and coach engineers to build strong testing capability across teams Key Responsibilities Define and deliver testing strategies … platforms) Proficiency in one or more: Python, Java, JavaScript, SQL, or R Proven experience in: AI/Generative AI testing RAG (Retrieval-Augmented Generation) solution validation (e.g. RAGAS framework) CI/CD, DevOps, and cloud-based test integration Ability to define and implement ...

practices within engineering teams Drive improvements in automation frameworks and testing tools Provide expert oversight on AI model validation, including Generative AI and RAG solutions Identify risks early and implement mitigation strategies Mentor and coach engineers to build strong testing capability across teams Key Responsibilities Define and deliver testing strategies … platforms) Proficiency in one or more: Python, Java, JavaScript, SQL, or R Proven experience in: AI/Generative AI testing RAG (Retrieval-Augmented Generation) solution validation (e.g. RAGAS framework) CI/CD, DevOps, and cloud-based test integration Ability to define and implement ...

Azure OpenAI. Key responsibilities include: Designing, developing and deploying AI‐enabled applications across cloud platforms Building and scaling Retrieval‐Augmented Generation (RAG) pipelines and agentic AI systems using trusted data sources Creating, testing and refining prompts, workflows and AI configurations to maximise accuracy ...

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 ...

/CD, DevSecOps and automated testing Experience working within Agile engineering teams Exposure to AI developer tools or commercial GenAI projects (LLMs, LangChain, RAG, chatbots or AI-powered features) is highly desirable What's on Offer Work on modern full-stack applications with exposure to AI technologies Collaborative, high-performing ...