26 to 50 of 57 Retrieval-Augmented Generation Jobs in Manchester

libraries (LangChain, LangGraph or related open-source toolkits strongly preferred. Background in Traditional ML/AI is preferred.Deep understanding of LLMs, prompt engineering, RAG pipelines, vector databases, and generative architectures; related security practices and evaluation procedures; hands-on experience fine-tuning, deploying and evaluating large-scale production systems.Hands-on experience ...

production-ready solutions. Key Responsibilities Design and develop AI-powered applications using GenAI , LLMs , NLP , and Agentic AI technologies. Build intelligent RAG (Retrieval-Augmented Generation) solutions leveraging Embeddings and Vector Databases . Develop and orchestrate AI Agents and Multi-Agent Systems to automate complex … Language Models (LLMs) , Natural Language Processing (NLP) Prompt Engineering , Context Engineering AI Agents , Agentic AI , Multi-Agent Systems LangChain , LangGraph , Model Context Protocol (MCP) RAG , Embeddings , Vector Databases Python , SQL , REST APIs Git , CI/CD Cloud Platforms (Azure, AWS, or GCP) LLM & AI Agent Testing and Evaluation Desirable Skills ...

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 ...

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 ...

science capability. And any experience of this would be really useful. Building agentic AI solutions, LLM‐powered applications and retrieval‐augmented generation (RAG) architectures. Experience with Vertex AI Agent Builder, Agentspace, LangGraph or multi‐agent frameworks. Knowledge of responsible AI, model governance and risk ...

product estate. Disciplined use of AI tools across the delivery process with the ability to validate output, supported by optional experience in production LLMs, RAG, and AI agents. Proven background in customer-facing technical delivery, spanning pre-sales technical discovery, hands-on implementation, and escalation management. Excellent communication skills ...

Matillion product estate.Disciplined use of AI tools across the delivery process with the ability to validate output, supported by optional experience in production LLMs, RAG, and AI agents.Proven background in customer-facing technical delivery, spanning pre-sales technical discovery, hands-on implementation, and escalation management.Excellent communication skills to connect with ...

libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps and production AI practices, including CI/CD, model versioning ...

LangGraph, including agent design, state/memory management, and graph-based orchestration.Proficiency in ML/NLP and generative models; experience with embeddings, vector stores, RAG, and LLM integration/fine-tuning (OpenAI, LLaMA, Cohere, etc.)Strong coding in Python and experience with frameworks/tools such as FastAPI, PyTorch/ ...

detection that balance performance, explainability, and operational risk. Lead a team of Data Scientists Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine-tuning of models. Collaborate with Data Engineers to build scalable, secure, and production-ready ML pipelines on GCP. Partner with cyber defence ...

PyTorch, TensorFlow, scikit-learn) and practical implementation of model development and deployment.Working knowledge of Generative AI, including large language models, prompt engineering, retrieval-augmented generation and evaluation approaches.Experience with MLOps and modern engineering practices, including CI/CD pipelines, model versioning, monitoring and automated ...

productive teams Experience leading teams in fast‐growing startups or enterprise software companies Knowledge of financial planning, analytics, or business intelligence domains Experience with RAG systems, vector databases, and semantic search Familiarity with model fine‐tuning, evaluation, and continuous improvement workflows Background in distributed systems, microservices, or cloud‐native architectures ...

Amazon Comprehend, Amazon Transcribe, Amazon Rekognition and related managed AI services.· Generative AI solution design, including foundation model selection, prompt engineering, retrieval-augmented generation, model evaluation, responsible AI controls and enterprise integration patterns.· Data and AI platform architecture on AWS, including data ingestion, governance ...

work forward independently. What makes you stand out? Experience building AI/LLM-enabled products in production or controlled enterprise environments (e.g. agentic workflows, RAG, prompt engineering, evaluation and governance patterns) Experience with React/Next.js and modern web tooling. Familiarity with document/NoSQL stores (e.g. MongoDB) and data ...

transformation programme. You will design and build scalable data pipelines in GCP, develop cloud-native solutions, and support AI/LLM and RAG initiatives across a large-scale environment. You will work on event-driven architectures with Kafka, write robust Python and SQL, and help ensure data quality, governance ...

across a range of AI and cloud projects. Responsibilities will include: Designing and delivering Generative AI and Machine Learning solutions Building applications using LLMs, RAG frameworks and AI orchestration tools Working directly with clients to understand business challenges and define AI-driven solutions Leading architecture and engineering decisions across Azure … experience building AI-powered applications and deploying them into production Software Engineering or Product Engineering background Strong Python development skills Experience working with LLMs, RAG architectures and modern AI frameworks Multi-cloud experience across Azure, AWS and GCP Strong understanding of cloud-native architectures and scalable application design Experience with ...

applications in production environments. Experience with vision‐language models or multimodal AI over images, video, inspection evidence, diagrams, screenshots, or technical records. Experience with RAG over structured and unstructured data. Familiarity with observability, log analysis, incident response, support tooling, runbooks, or developer tools. Experience building workflow UIs where AI assists ...

develop new skills and hone existing ones. We work for a huge variety of interesting, international, and world-leading clients on exciting next generation technology-enabled projects. We are looking for candidates with AI/GenAI Architect based expertise to join us, working in fast-paced teams with … content around data design, architecture and engineering like collateral and playbooks on KX, improving our artefacts and strategies.Knowledge of generative AI architectural patterns including RAG & fine tuning.Experience training & deploying machine learning models using a range of techniques (e.g. Classification, Regression, Neural Networks, etc).Experience with developing and deploying applications ...

grade deployment. The role: - Design AI and GenAI architecture strategies and roadmaps - Build scalable AI/ML and Agentic AI platforms - Work across LLMs, RAG, embeddings and semantic search - Support AI deployment, governance and integration patterns - Collaborate with engineering, data, DevOps and business teams - Advise clients on AI platform capability … implementation strategy Tech environment: Python LLMs (prompt engineering, fine-tuning, RAG) LangChain/LangGraph Agent frameworks Vector databases MLOps/LLMOps AWS/Azure/GCP/Databricks Kubernetes & GPU infrastructure What you'll bring: - Experience designing enterprise AI or ML architectures - Strong grasp of cloud and modern data platforms ...

Hugging Face), advanced GenAI frameworks (LangChain, HF Transformers, LlamaIndex) & Agentic architectures (LangGraph, SmolAgents, PydanticAI) Prompt Engineering: Deep prompt engineering & optimization skills for GenAI.Vector DBs & RAG: Designed solutions using vector DBs & Retrieval Augmented GenAI (RAG) for knowledge applications.GenAI ModelOps/MLOps & Governance: GenAI ModelOps/MLOps knowledge … with ethical AI governance focus.Multimodal & Code Generation: Architected GenAI for multimodal (vision, audio, language) & code generation.ER&I GenAI Applications: Applied GenAI to ER&I use cases to create business value.Enterprise Software Integration: Designed GenAI integrations with SaaS/ERP for business process automation.GenAI Impact Reporting: Designed advanced reporting ...

build and deploy enterprise-grade AI solutions across an Enterprise Knowledge Base (EKB). This is a hands-on role focused on GenAI, LLMs, RAG and Agentic AI, taking solutions from prototype through to production. You'll work across AI, data and engineering teams to build intelligent retrieval, knowledge discovery and automation capabilities. Key Responsibilities Build production-grade GenAI, LLM, RAG and Agentic AI solutions Develop AI Agents and Multi-Agent Systems Work with LangChain, LangGraph and MCP Build intelligent retrieval using Embeddings, Vector Databases and Semantic Search Apply Prompt & Context Engineering Integrate ...

Developing AI solutions that can reason, retrieve information, make decisions and take actions Working with business data to develop intelligent applications and automation Developing RAG, vector search, embeddings and knowledge-based AI solutions Evaluating new LLMs, AI frameworks and emerging technologies Taking AI concepts from initial idea through to production … following: Agentic AI/AI Agents LLM application development OpenAI, Azure OpenAI, Anthropic, Gemini or similar LLM orchestration and tool/function calling RAG and retrieval-based AI Vector databases and embeddings AI workflow automation Python, C# and/or TypeScript APIs and system integration SQL/databases ...

including API design, automated testing, asynchronous processing, dependency management and production debugging. Practical experience with agent orchestration, structured tool calling, workflow state, retrieval-augmented generation and human-in-the-loop patterns. Experience building secure integrations with enterprise APIs, repositories, cloud services, CI/ ...

platform that underpins our clinets conversational assistant and wider AI initiatives. You will take responsibility for the runtime, infrastructure, and operational foundations underpinning our RAG pipelines, LLM orchestration, and vector search, built across Microsoft Azure and Microsoft Foundry. Where prototypes and proof-of-concepts exist, you will … Build and extend a scalable AI platform via Microsoft Foundry. Own and optimise Foundry solutions for accuracy, scale, and latency. Harden the production RAG pipeline, covering inference, retrieval, and ingestion. Architect Azure services using Terraform infrastructure-as-code. Scale vector database and embedding pipelines. Build ...