126 to 150 of 252 Retrieval-Augmented Generation Jobs in England

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 ...

/EKS, Lambda, Bedrock, Docker). Notable understanding and experience building generative AI applications (LangChain, AutoGen, CrewAI or similar). Validated production experience with RAG, agent, and MCP‐based systems, including AgentOps (CI/CD, monitoring, optimisation). Pragmatic builder/hacker mindset with rapid iteration from idea to proof ...

/EKS, Lambda, Bedrock, Docker). Notable understanding and experience building generative AI applications (LangChain, AutoGen, CrewAI or similar). Validated production experience with RAG, agent, and MCP-based systems, including AgentOps (CI/CD, monitoring, optimisation). Applied, pragmatic “builder/hacker” mindset with rapid iteration (idea → POC → production. ...

developing Go-based microservices for scalable, high-throughput architecturesExperience in cybersecurity, SIEM, SOAR, or adjacent security domainsHands-on experience with LLM integration, prompt engineering, RAG pipelines, or agentic AI frameworks#LI-GO1#LI-HYBRIDBenefits of Working at CrowdStrike:Market leader in compensation and equity awardsComprehensive physical and mental wellness programsCompetitive vacation ...

APIs, workflows, business systems and human‐in‐the‐loop processes Strong understanding of LLM capabilities and limitations, including prompt design, retrieval‐augmented generation, hallucination management, tool use, evaluation and monitoring Experience with AI governance and responsible AI, including agent governance, access controls, data protection ...

similar platforms. Exposure to cloud technologies, APIs and solution integrations. Knowledge of Python, scripting or other programming languages. Understanding of prompt engineering, agent orchestration, RAG or AI evaluation techniques. Experience supporting AI capability building, training or adoption initiatives. Knowledge of AI governance, risk management and responsible AI principles. ...

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 ...

/CD pipelines (Azure DevOps). * Demonstrated hands-on exposure to at least one of: LLM API integration, vector databases, prompt engineering, or RAG pipeline construction * Familiarity with REST-based AI APIs (OpenAI, Anthropic, Google Vertex AI, or equivalent). * Basic understanding of embeddings, semantic search, and token-based context ...

/CD pipelines (Azure DevOps). * Demonstrated hands-on exposure to at least one of: LLM API integration, vector databases, prompt engineering, or RAG pipeline construction * Familiarity with REST-based AI APIs (OpenAI, Anthropic, Google Vertex AI, or equivalent). * Basic understanding of embeddings, semantic search, and token-based context ...

Extensive Experience: Proven track record of leading the delivery and maintenance of complex software products in a DevOps and Agile environment.Deep practical experience with RAG, LLMs, and MLOps.Expert-level understanding of NLP, Embeddings, and Prompt Engineering.Expert proficiency in Python, React, and TypeScript.Significant experience with Cloud platforms (GCP or Azure ...

production-grade LLM systems with a strong focus on performance, reliability, security, and cost-efficiency Work extensively with unstructured data using RAG (Retrieval-Augmented Generation), alongside structured (SQL) and time-series/streaming data Develop robust APIs and integration layers for LLM capabilities Collaborate ...

agentic AI workflows that automate and streamline complex business processes. Design and apply advanced techniques such as semantic search, retrieval‐augmented generation (RAG), named entity recognition (NER), prompt engineering, and personalization for content extraction, search, question answering, reasoning, and recommendation. Develop ...

learning solutions Practical experience with Large Language Models (LLMs), Generative AI and prompt engineering Knowledge of modern AI tooling and frameworks, including exposure to RAG architectures and vector databases Experience working with cloud platforms, ideally Google Cloud Platform (GCP) Strong software engineering fundamentals and experience developing scalable applications Excellent communication ...

coding standards and best practices · Guide evaluation and safety approaches · Support production deployments and troubleshooting Hands-on Skills Required · Design multi-agent solutions · Design RAG architecture · Implement complex orchestration workflows · Build scalable AI microservices · Define AI governance patterns · Troubleshoot production AI solutions ...

coding standards and best practices Guide evaluation and safety approaches Support production deployments and troubleshooting Hands-on Skills Required Design multi-agent solutions Design RAG architecture Implement complex orchestration workflows Build scalable AI microservices Define AI governance patterns Troubleshoot production AI solutions Technologies Azure OpenAI Azure AI Foundry Semantic Kernel ...

Experience with MCP and Vertex/storage-related infrastructure Strong understanding of AI architecture, orchestration, and scalable deployment practices Preferred Experience Building AI agents, RAG pipelines, or enterprise AI applications Working with cloud-based AI/ML platforms and data storage solutions Collaborating with engineering and product teams to deliver ...

future of AI-powered software engineering. You'll be working across: Python & FastAPI React & TypeScript AWS & Docker CI/CD & Cloud Infrastructure LLMs, RAG, Agentic AI & AI-powered workflows You'll thrive if you: Have built and shipped production applications Enjoy solving business problems, not just writing code Take ownership ...

some of the following technologies or ecosystems: Amazon Bedrock and SageMaker Claude/Anthropic Azure AI Foundry or Azure OpenAI Databricks Vector databases and RAG architectures Agentic AI orchestration frameworks and MCP servers Model and data lifecycle management Desirable Experience Experience working within public sector or regulated environments Experience with ...

Lean Six Sigma Black Belt. Experience building process digital twins and always-on conformance/monitoring capabilities. Familiarity with GenAI/LLMs, NLP, CV, RAG, knowledge graphs, and MLOps/LLMOps, and how they reshape process design. Knowledge of emerging regulatory expectations for AI in regulated industries. Risk, monitoring, bias ...

deploy AI solutions that solve high-priority business problems. Evaluate and integrate existing AI tools, frameworks, and APIs (e.g., LLMs, vector DBs, retrieval-augmented generation, AI agents) into robust applications. Champion automation in workflowsfrom data management ingestion and preprocessing to evaluation, to model integration … Familiarity with AI/ML tools such as LangChain, Haystack, Hugging Face, Weaviate, or similar ecosystems. Hands-on experience with Retrieval Augmented Generation applications, AI agents and systems built around them. Experience using GenAI frameworks such as LlamaIndex, Crew AI, AutoGen, or similar agentic ...

organisation. You'll be responsible for Defining engineering standards for enterprise AI Driving adoption of Agentic AI across multiple engineering teams Designing production-grade RAG architectures Establishing LangGraph and LangChain implementation patterns Designing scalable vector database retrieval strategies Leading best practice across MLOps, CI/CD and model … engineering stakeholders without direct line management We're looking for someone with deep experience in: Python AWS LangChain LangGraph Retrieval Augmented Generation (RAG) Vector databases MLOps Production Machine Learning platforms AI governance and engineering best practice PyTorch, TensorFlow or Scikit-learn More important than ...

scripting languages (e.g., Python) Author, optimize, and manage system prompts for customer-facing LLM systems Integrate API calls into Retrieval Augmented Generation (RAG) systems Evaluate model performance and annotation quality to produce reports for stakeholders Produce, process, and manipulate different types of language data … Familiarity with AI coding assistants Preferred Qualifications PhD in Applied Linguistics, Computational Linguistics, NLP, or related field Experience with SQL and Git Experience building RAG or agentic systems Experience conducting quantitative analysis Experience building data pipelines Experience with AWS services (Bedrock, S3, EC2, etc.) Knowledge of user experience concepts ...

platforms such as AWS, GCP, or Azure. What you’ll bring Deep, hands‐on experience building and deploying production‐grade AI systems using LLMs, RAG pipelines, MCPs, and agentic workflows Proven track record of leading complex, client‐facing engagements with a forward‐deployed mindset, partnering with both technical and business ...

Strong Python engineering capability Experience deploying AI products into real-world environments Experience with several of: LLM applications Agentic AI frameworks Multi-agent systems RAG architectures Vector databases AI orchestration platforms AI evaluation and observability tools Model optimisation and fine-tuning Mindset We're particularly interested in individuals who: Thrive ...

Product management, AI Strategy and engineering Hands-on experience building and scaling AI or machine learning products in production environments. Experience understanding LLMs RAG Architecture and agentic workflows Excellent experience with Python. Excellent experience working with AI platforms (Claude, Open AI and AI orchestration frameworks) Experience within Financial services. ...