151 to 175 of 419 Retrieval-Augmented Generation Jobs in the UK

/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 ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

Role We're scaling the backend systems powering our AI-driven product suite, including retrieval-augmented generation (RAG) pipelines, real-time inference APIs, and vector search infrastructure click apply for full job details ...

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 ...

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 ...

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 ...

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 ...

structured, semi-structured and unstructured data OR 5+ years ML Engineering or AI Engineering deploying, promoting and serving ML models, building feature stores, building RAG chatbot or other AI systems Hands on, working, knowledge of Databricks ML and Gen AI features is required Expertise in software engineering or DevOps concepts ...

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. ...

FastAPI. Proven track record of delivering production software on client-facing projects, ideally within a consultancy or professional services environment. Experience with prompt engineering, RAG architectures, or agentic AI development at scale. Familiarity with AI-assisted development tools such as GitHub Copilot, Claude, and Cursor. Strong hands-on experience with ...

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 ...

fast-moving environment and take ownership of complex technical problems. Desirables: Experience working in healthcare or regulated environments Exposure to LLM workflows, RAG systems, or AI orchestration platforms Experience with event streaming, message queues, and distributed systems tooling Understanding of compliance standards such as SOC2, ISO27001, HIPAA, or GDPR 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 ...