51 to 75 of 236 Retrieval-Augmented Generation Jobs in London

from idea to production. Strong knowledge of deep learning, NLP, and LLM architectures and experience in fine‐tuning LLMs or training domain‐specific models, RAG, embeddings, and vector databases. Familiarity with agentic AI concepts and orchestration frameworks. Experience deploying AI solutions on cloud platforms such as Azure, AWS, or GCP. ...

across the others: Agentic AI & LLM Engineering Design and build production-grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM-powered systems; current knowledge of the latest model capabilities ...

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 ...

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 ...

border data residency and audit requirements). AI Foundations: Experience designing agentic or AI‐enabled solutions, including patterns such as retrieval‐augmented generation, tool and API integration, evaluation and human‐in‐the‐loop design, would be a significant advantage, though it is not essential. ...

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 ...

MLflow, Weights & Biases or Data Version Control Experience designing, testing and evaluating machine learning experiments Knowledge of Generative AI, LLMs, prompt engineering and RAG architectures Experience with LLMOps tools such as LangChain, LangSmith or LangGraph Strong understanding of model monitoring, validation and governance Excellent communication and stakeholder management skills Ability ...

/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-18808-Ljbffr ...

/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 ...

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 ...

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. ...

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 ...