18 of 18 Retrieval-Augmented Generation Jobs in Manchester

Design and build AI-enabled platforms and intelligent workflows using large language models (LLMs), retrieval-augmented generation (RAG) pipelines and embedding models. Develop scalable backend services and AI integrations, working closely with engineering, architecture and business teams, and support deployment, governance and operational rollout … infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps and LLMOps familiarity expected. Strong communication ...

Senior AI Engineer to design, build and scale Agentic AI, Generative AI and Retrieval-Augmented Generation (RAG) solutions that power intelligent, customer-facing products used globally. Working closely with Product, Platform and Engineering teams, you'll develop production-grade AI systems, autonomous agents … applications, helping shape the future of AI across the organisation. What You'll Do Design and deliver production-ready Agentic AI, Generative AI and RAG solutions. Build autonomous AI agents capable of reasoning, planning and executing complex workflows. Develop multi-agent orchestration, tool integration and advanced retrieval strategies. ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

evolve and pragmatic decision-making is essential. Nice to Have Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures. Experience working with containerised applications and modern CI/CD pipelines. AWS certifications, including Solutions Architect or Machine Learning Specialty. ...

evolve and pragmatic decision-making is essential. Nice to Have Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures. Experience working with containerised applications and modern CI/CD pipelines. AWS certifications, including Solutions Architect or Machine Learning Specialty. ...

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 ...

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 ...

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 ...

prototypes, products and production ready solutions ·Designing and implementing end to end AI solutions that integrate with enterprise systems ·Working with LLMs, prompt engineering, RAG patterns, embeddings and fine tuning ·Developing AI agents and agentic workflows using modern frameworks ·Working with vector databases, APIs and modern data platforms ·Supporting … collaborative, high performing environment Experience Required: ·Strong Python and SQL experience ·Applied AI engineering, ML engineering or software engineering background ·Experience with LLMs, RAG, embeddings, prompt engineering or fine tuning ·Exposure to LangChain, LangGraph, Agent Development Kit or similar agent frameworks ·Experience with vector databases such as Pinecone, Chroma ...

SeniorAIEngineerManchester Agrowingtechnology-ledbusinessisseekinga SeniorAIEngineer toplayapivotalroleinshapinganddeliveringitsAIstrategy.Thisisasenior-level,hands-onpositionresponsiblefordesigning,building,anddeployingenterprise-gradeAIsolutionsthatdrivemeasurablebusinessvalueacrosstheorganisation. WorkingcloselywithIT,Data,Engineering,andseniorbusinessstakeholders,youwillleadthedevelopmentandimplementationofadvancedAIsolutions,leveragingLargeLanguageModels(LLMs),Retrieval-AugmentedGeneration(RAG),AIAgents,MachineLearning,andAzureAIservices.Youwillprovidetechnicalleadership,establishbestpractices,andhelpbuildtheorganisationslong-termAIcapability. ThebusinessisinvestingheavilyinAIandislookingforsomeonewhocancombinedeeptechnicalexpertisewithstrategicthinking,takingsolutionsfromconceptthroughtoproductiondeploymentandadoption. TheidealcandidatewillhaveastrongfoundationinAIEngineeringcombinedwitharobustDataSciencebackground,enablingthemtodevelop,evaluate,andoptimiseAIsolutionsthatdelivermeasurablebusinessoutcomes. ThisrolewouldsuitanexperiencedAIprofessionalwhoenjoyssolvingcomplexbusinesschallenges,mentoringothers,influencingtechnicaldirection,anddeliveringinnovativeAIsolutionsatscale. RolePurpose AsaSeniorAIEngineer,youwillleadthedesign,development,deployment,andoptimisationofenterpriseAIsolutions,ensuringtheyaresecure,scalable,maintainable,andalignedtobusinessobjectives. YouwillactasasubjectmatterexpertforAItechnologies ...

shape strategy and drive delivery within a large, complex organisation Knowledge of modern AI approaches, including areas such as Retrieval Augmented Generation (RAG), prompt engineering, observability and evaluation Experience in cyber security, fraud analytics, behavioural analytics or threat detection would be particularly valuable. This ...

Skills Strong banking domain expertise (retail, lending, payments, contact center) Expertise in Conversational AI/IVR and GCP architecture Knowledge of Agentic AI, RAG, and API-led integrations Exceptional stakeholder engagement and influencing skills Experience 1218+ years in enterprise architecture/solution design Proven experience in onsite client-facing roles ...

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 ...

teams. Design and deliver Voice AI/Agentic IVR solutions leveraging: Google CES/CXAS, Dialogflow CX/CCAI Vertex AI (LLMs, RAG, agent frameworks) Define and implement enterprise data architecture for Voice AI: Conversation data pipelines (real-time + batch) Integration with data lakes, warehouses (BigQuery) Customer … contextual data enablement Build RAG-based knowledge systems integrating structured and unstructured banking data. Architect data-driven decisioning for voice agents (personalization, next-best action, fraud detection signals). Ensure integration with core banking, CRM, and analytics platforms . Establish data governance, lineage, quality, and compliance frameworks (GDPR ...

production-ready. Technology The successful candidate will work with modern cloud platforms (AWS or similar), vector databases, knowledge graphs, retrieval systems, embeddings, RAG architectures, data pipelines, APIs and contemporary AI tooling. What they're looking for Strong commercial experience in senior data engineering, knowledge engineering or a closely … technologies and architectures rather than defaulting to a single solution. Strong software engineering and cloud platform fundamentals. Nice to have Experience with production-scale RAG architectures and retrieval evaluation. Knowledge of emerging open knowledge standards and interoperable knowledge formats. Experience with context engineering for AI agents ...

autonomy to build and shape the AI function as your own. What You'll Do Lead the design and deployment of a market-leading RAG conversational agent, working with a large, complex proprietary dataset. Partner with product managers, content managers and senior stakeholders to design, develop, deploy and optimise state … user requirements. Optimise LLMs, datasets and agent workflows to the highest quality standards, detecting and mitigating hallucinations and output errors. Deploy and maintain RAG architecture and LLM pipelines within cloud environments. Optimise data-chunking and text embeddings for optimal semantic search across a large, specialist corpus. Track the competitive landscape ...

operate hands‐on where needed. Proven experience leading teams of Principal Engineers and Architects in complex environments. Strong understanding of AI integration patterns (e.g. RAG, embeddings, vector databases), cloud‐native architectures, and scalable distributed systems. Experience overseeing AI‐enabled product capabilities at scale. Proven ability to drive high standards … software architecture patterns, including microservices, event‐driven systems, and cloud‐native design. Strong understanding of AI/ML concepts and integration approaches, including LLMs, RAG architectures, vector databases, and responsible AI practices. Expertise in scalable platform design, performance optimisation, and distributed systems used by high‐volume, global user bases. Solid ...