26 to 50 of 69 Retrieval-Augmented Generation Jobs in the North West

Build and integrate AI-powered features into web applications and enterprise tools. Build and optimize Retrieval-Augmented Generation (RAG) solutions to power chatbot interactions Work both independently and paired with other team members to ensure efficient delivery of prioritised features. Take part … Experience using modern AI-assisted and agentic development workflows in day-to-day engineering usingAI/ML APIs and SDKs Hands-on experience developing RAG solutions or working with LLMs for chatbots is preferred In-depth understanding of CI/CD and the tools used to implement them, such ...

both customers and organisations. Modern AI architecture expertise. Deep understanding of machine learning, LLMs, retrieval-augmented generation (RAG), agentic systems and the architectural patterns used to deploy AI solutions at enterprise scale. Engineering and cloud foundations. Strong understanding of software engineering, data architecture ...

value for both customers and organisations.Modern AI architecture expertise. Deep understanding of machine learning, LLMs, retrieval-augmented generation (RAG), agentic systems and the architectural patterns used to deploy AI solutions at enterprise scale.Engineering and cloud foundations. Strong understanding of software engineering, data architecture ...

help build the data foundations that power enterprise search, knowledge discovery, AI applications and Retrieval-Augmented Generation (RAG). What you'll be doing Design and build scalable ETL/ELT data pipelines across multiple enterprise data sources Develop data solutions using BigQuery, Google ...

products and wider AI strategy.You'll design, build and operate the cloud-native platform supporting Retrieval-Augmented Generation (RAG), LLM orchestration, vector search and production AI services, working closely with AI Engineering, DevOps and Security teams to deliver scalable, secure and reliable AI solutions. … Role scope: Designing, building and operating AI platform infrastructure across Microsoft Azure. Developing and optimising RAG pipelines, AI services and platform components. Deploying infrastructure using Terraform and supporting containerised applications. Building and maintaining CI/CD pipelines using GitHub and Azure DevOps. Improving observability, monitoring and platform resilience. Supporting vector ...

within SAP consulting, working with the latest AI agent frameworks, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines to help clients unlock new value, streamline complex processes, and accelerate their digital transformation journeys. This is more than a delivery role. … prioritised, and your career can accelerate quickly. What makes this role different? A specialist innovation focused on SAP consulting, working with AI agents, LLMs, RAG pipelines, and agentic workflows rather than traditional SAP configuration. Exposure to high-profile, complex transformation programmes across multiple industries, delivering cutting-edge technology solutions alongside ...

most cutting-edge defence and aerospace projects in the UK? If the prospect of developing software for projects such as a sixth-generation stealth fighter jet or a revolutionary Laser Directed Energy product excites you, then Leonardo would like to hear from you. Leonardo is seeking to recruit … fine-tuning large language models (LLMs). Experience building scalable pipelines, including APIs, vector databases, retrieval-augmented generation (RAG), and orchestration frameworks. Experience curating datasets, managing embeddings, and ensuring security, privacy, and responsible AI practices. Experience in prompt engineering, with the ability to design ...

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 ...

Forward Deployment Engineer, you should have experience with:Good technical experience in AI/ML with experience developing and deploying solutions using LLMs, RAG architectures, and modern AI frameworks and protocols (Strands, FastMCP, LangChain, LlamaIndex, Hugging Face, A2A, MCP).Ability to translate multi-layered business problems into technical solutions with ...

Forward Deployment Engineer, you should have experience with: Good technical experience in AI/ML with experience developing and deploying solutions using LLMs, RAG architectures, and modern AI frameworks and protocols (Strands, FastMCP, LangChain, LlamaIndex, Hugging Face, A2A, MCP).Ability to translate multi-layered business problems into technical solutions with ...

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 ...

learning or Generative AI to be a genuine peer to your architects and engineers. You should be able to explain, without notes, how a RAG architecture fails in production and what you would do about it. • Team leadership. Experience leading and coaching consultants, engineers or architects within your programme ...

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 ...