51 to 75 of 214 Retrieval-Augmented Generation Jobs in England

delivery of AI and cloud-native architectures, including Generative AI, NLP, and LLM solutions. Retrieval-Augmented Generation (RAG) and CAG (Cache Augmented) Architecture: Defining architectural patterns for end-to-end pipelines Act as a trusted advisor to senior stakeholders, guiding AI roadmaps ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

discuss flexible working! Top 3 skills needed for this role: Expert AI/ML Skills – Strong in both traditional ML and modern LLM/RAG/agentic workflows Production Deployment & MLOps – Comfortable shipping scalable AI systems using APIs, microservices, Docker/Kubernetes, and cloud (GCP ideal) Business Partnering – Able … based applications. The role involves building internal and external AI solutions such as Retrieval-Augmented Generation (RAG) systems, chatbots, and voice-based customer service platforms, all deployed on Google Cloud Platform (GCP). You will work closely with data scientists, software engineers, and product ...

discuss flexible working! Top 3 skills needed for this role: Expert AI/ML Skills Strong in both traditional ML and modern LLM/RAG/agentic workflows Production Deployment & MLOps Comfortable shipping scalable AI systems using APIs, microservices, Docker/Kubernetes, and cloud (GCP ideal) Business Partnering Able … based applications. The role involves building internal and external AI solutions such as Retrieval-Augmented Generation (RAG) systems, chatbots, and voice-based customer service platforms, all deployed on Google Cloud Platform (GCP). You will work closely with data scientists, software engineers, and product ...

managing multi-API workflows using tools like LiteLLM to ensure flexibility and resilience. Building sophisticated Retrieval-Augmented Generation (RAG) systems, leveraging advanced techniques like embeddings with Voyage AI, rerankers , and query enrichment . Designing and maintaining efficient data pipelines and vector storage solutions using … PyTorch. Proven experience building and deploying applications with LLM APIs such as OpenAI , Anthropic , Gemini , and DeepSeek . Hands-on experience with the full RAG pipeline, including vector embeddings , rerankers , and data indexing in databases like MongoDB. Practical knowledge of LLM fine-tuning, prompt engineering, and performance optimization. Familiarity with ...

architecture and hands-on delivery across Generative AI, Large Language Models (LLMs), Agentic AI and Retrieval-Augmented Generation (RAG). Key Responsibilities: Lead AI strategy and executive advisory engagements Architect enterprise-grade Generative AI and Agentic AI solutions Design multi-agent systems, orchestration frameworks … RAG pipelines Own end-to-end solution architecture from discovery to implementation Shape AI-led deal pursuits, presales and business cases Define AI operating models, governance and responsible AI frameworks Integrate AI solutions with ERP, CRM and enterprise platforms Required Experience: Strong consulting background in AI/digital transformation Deep ...

cloud services to meet functional and non-functional requirements. Lead architectural design decisions for LLM/SLM deployments, including fine-tuning, inference optimisation, RAG frameworks, and API-based AI services. Define AI landing zones, governance frameworks, and security controls aligned to enterprise standards. Ensure solutions integrate seamlessly into existing … designs. Strong expertise in AI architecture including: Large and Small Language Models (LLMs/SLMs) Retrieval-Augmented Generation (RAG) Model fine-tuning and inference optimisation Model orchestration and API-driven AI deployment Experience working within a technology focused consulting business. Deep knowledge of hyperscaler ...

investment banking, asset management, or capital markets domains.* Deep expertise in ML, NLP, LLMs, retrieval-augmented generation (RAG), embeddings, and modern MLOps practices.* Strong experience working with both structured financial datasets and unstructured data sources (e.g., filings, call transcripts, research).* Familiarity with front ...

scaling Support experimentation, benchmarking, and cost/performance analysis of AI models Contribute to retrieval-augmented generation (RAG) solutions and data integration patterns Establish reusable API standards, frameworks, and documentation to support wider adoption Troubleshoot and optimise distributed systems and cloud-based services Core ...

monitoring. Start date: February 2026 Responsibilities: Developing new applications to support marketing performance and automation Build AI-powered prototypes and applications using LLMs, LangChain, RAG pipelines, and other emerging frameworks. Experiment with new AI technologies (e.g. vector databases, embedding models, prompt optimisation, AI agent, Google AI Studio/Gemini ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

realisation across complex technology estates. Key Responsibilities AI Engineering & Delivery Design, build, and deploy production‐grade AI/ML solutions , including: LLM‐enabled applications (RAG, agents, copilots) Predictive models and intelligent automation AI services embedded into modernised platforms Own end‐to‐end AI lifecycle : discovery, PoC, productionisation, monitoring, and optimisation … least one of: TensorFlow, PyTorch, scikit‐learn Practical experience with LLMs and Generative AI , including: Prompt engineering Retrieval‐Augmented Generation (RAG) Agentic workflows Experience deploying AI on cloud platforms (Azure, AWS or GCP) Containerisation and orchestration (Docker, Kubernetes) M&A & Enterprise Transformation Proven experience ...

party vendors. Knowledge of OO programming and software design, i.e., SOLID principles. Knowledge and working experience of AGILE methodologies. Familiarity with Databricks, and RAG application architecture a plus Experience with latency optimisation and quantisation preferred Additional Information What’s in it for you?: Competitive salary that reflects your skills, experience ...

featuresfrom data ingestion and feature engineering to model training, evaluation, and deployment. Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings, vector search, caching, re ranking). Optimise training and inference performance via batching, quantisation, distillation, LoRA/PEFT, accelerator utilisation (GPU/… building GenAI applications using Python Hands-on Experience with API Development and Microservices architecture and End to End integrations Knowledge of RAG (Retrieval-Augmented Generation) and ADK, MCP Solid understanding of LLMs, prompt engineering, and graph-based workflows. Hands-on Experience with API Development ...

foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within ...

foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within ...

ideal for someone who excels at technical storytelling and enjoys translating complex concepts-such as retrieval-augmented generation (RAG), vector search, and large language model (LLM) integrations-into engaging, developer-focused multimedia content. You will play a key role in shaping video strategy, producing … curate a content calendar with a strong focus on video and demo assets Script, produce, and edit technical videos (e.g. step-by-step RAG demos, vector search walkthroughs, LLM integration tutorials) using tools such as Camtasia or similar Collaborate closely with engineers, developer advocates, and demo creators to ensure technical ...

across the digital twin pipelines Nice to have: Building agents/multi-agent systems Developing Retrieval-Augmented Generation (RAG) systems Agentic frameworks: LangGraph/LangChain Deployed LLMs at enterprise scale Modis International Ltd acts as an employment agency for permanent recruitment and an employment ...

hands on experience in deploying and hosting Large Foundational Models. Experience with LLM architecture (e.g., Transformer, GANs, VAEs), Fine-tuning, Retrieval Augmented Generation techniques and Context embedding, Vector database technologies and Semantic Search techniques & tools. Experience in writing robust and efficient code in Python. ...

hands on experience in deploying and hosting Large Foundational Models. Experience with LLM architecture (e.g., Transformer, GANs, VAEs), Fine-tuning, Retrieval Augmented Generation techniques and Context embedding, Vector database technologies and Semantic Search techniques & tools. Experience in writing robust and efficient code in Python. ...

following: You are good at prototyping and experimenting with emerging technologies to assess feasibility and impact. You have knowledge of vector databases, RAG architectures and their integration into enterprise systems You have experience with GenAI development frameworks or orchestration tools such as Semantic Kernel, AutoGen, LangGraph, LangChain, or PromptFlow. ...

healthtech Deployed LLMs or predictive models in production environments Familiarity with FHIR, HL7, or secure health data pipelines Hands-on LLM work: prompt engineering, RAG, fine-tuning (LoRA/QLoRA) The Package Competitive salary + meaningful equity Work directly with and be mentored by two highly experienced co-founders Spacious ...

develop GenAI applications for Code Translation, Text Extraction, Summarisation and SDLC Optimization etc. Hands-on Experience with AI Agents, Chat bots, RAG (Retrieval-Augmented Generation), and vector databases. (PG vector/croma DB) Hands-on Experience with GenAI Performance Evaluation tools like Pegasus, Ragas ...

Understanding of MLOps, model deployment and performance optimisation Ability to work in a consultancy environment with multiple clients Desirable: Experience with LLMs, RAG pipelines or generative AI applications Knowledge of Docker, Kubernetes or CI/CD for ML Exposure to other cloud platforms (Azure/GCP) Whats on Offer ...