101 to 125 of 214 Retrieval-Augmented Generation Jobs in England

services on cloud infrastructure (GCP, AWS, and Azure) · High ownership mindset: you care deeply about reliability, correctness, and outcomes Strong Plus · Experience with LangGraph, RAG pipelines, vector databases, or knowledge graphs · Familiarity with Neo4j or graph-based data modelling · Experience working on AI-native or agentic systems · Comfort working ...

Role 2: Applied AI Engineer Strong backend engineering experience (Python preferred) Deep knowledge of CI/CD, Docker, Kubernetes, and Git workflows Experience building RAG pipelines and AI agent orchestration systems Minimum 5 years of experience 📩 Interested? Message me here or email mmatysik@trg-uk.com ...

Develop production-ready AI agents on GCP, including secure API integrations. Integrate agent capabilities into applications using MCP servers and supporting tools. Develop reliable RAG pipelines, including ingestion, chunking, metadata and vector search. Required: Strong Python skills for backend, APIs, testing and async patterns. Practical experience with LLMs, embeddings, prompt ...

Mistral, or similar) • Familiarity with API development and modern software architecture • Understanding of prompt design and managing AI-generated outputs • Ideally have experience in RAG pipelines and vector databases (e.g. Pinecone, Weaviate, pgvector) • Familiarity with cloud platforms is advantageous (AWS, Azure or GCP) • A proactive mindset with the ability ...

consultative mindset, able to set architectural direction and foresee emerging trends. Advanced knowledge of AI and ML technologies, including generative AI, agentic architectures, RAG pipelines, LLM integration, and responsible AI governance. Strong multi-cloud expertise with GCP, AWS, and Azure, including serverless, event-driven architectures, IaC, and container orchestration. Proven ...

catalog, data marketplace, feature store, or developer portals (e.g., Kaggle Datasets, Unity Catalog, Collibra, Alation, Atlan). Understanding of agentic AI patterns — tool-use, RAG, function calling — and how marketplace APIs can be exposed to LLM-based agents. Experience with search relevance & recommendation systems — ranking algorithms, semantic search, personalisation. Hands ...

/experience/knowledge: Experience in architecting and solutioning in Gen AI, Agentic AI, classic ML, and automation space. Good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability. Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face ...

AutoGen for reasoning, orchestration, and tool-use. Build intelligent document processing workflows leveraging AWS Textract , Comprehend , and extraction pipelines. Implement GenAI automation patterns including RAG , summarisation, classification, and routing using AWS Bedrock . Build monitoring and automation insight dashboards using AWS QuickSight . Write clean, modular Python code for APIs ...

Hybrid Senior engineer/architect responsible for building Agentic AI capabilities within the SDLC platform environment. Key skills: Agentic workflows SLM, Vector DB, MCP, RAG GitHub Copilot/AI developer tooling Cloud-native engineering GCP preferred CI/CD platforms Harness preferred Strong GitOps and engineering governance 3? Agentic SDLC ...

Infrastructure as Code, with a strong focus on automation, compliance and best practice. Develop and integrate AI applications and services, including classical ML, NLP, RAG and generative AI solutions. Support experimentation and proof-of-concept work through secure AI sandboxes and evaluation of emerging tools and platforms. Monitor, optimise ...

shipping reliable systems used by real customers. Key responsibilities Build and deploy LLM-powered features across drafting, prosecution, and litigation workflows Design and maintain RAG pipelines over large, complex legal and technical corpora Own systems from prototype through to production Focus on robustness, evaluation, and reliability in precision-critical ...

governance, auditability, and model transparency requirements. Proven ability to work deeply with engineering and data science teams on APIs, data models, system architecture, LLMs, RAG systems, and probabilistic modelling. Strong technical fluency, able to evaluate trade-offs between modelling accuracy, latency, scalability, and usability in production environments. Structured, execution-focused ...

with LLMs, ensuring that AI features are functional, brand-safe, and technically sound. KEY RESPONSIBILITIES: Design and implement agentic workflows and prompt strategies. Develop RAG systems to allow AI models to interact with specific client data sets. Optimize AI performance, focusing on token usage, latency, and response accuracy. Work within ...

unified decisions. Own operability: CI/CD, monitoring/alerting, performance optimisation, safe rollbacks. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Help evolve our reusable product templates and shared architecture across the portfolio. Essential Requirements ...

solutions aligned to client business goals. Build scalable architectures using modern cloud platforms ( Azure, AWS or GCP ). Lead the design of RAG-based solutions, AI assistants, and autonomous agent systems . Technical Leadership Guide the end-to-end delivery of complex AI implementations. Evaluate and select models, frameworks … capability. Key Skills & Experience Proven experience architecting Generative AI, Agentic AI, machine learning and automation solutions in enterprise environments. Strong expertise in LLMs and RAG architectures , including models such as OpenAI, LLaMA, Mistral or Claude. Hands-on experience with GenAI frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI or LangGraph . ...

hosting large foundation models. Experience with large language model architectures such as transformers, GANs, and VAEs, along with fine-tuning techniques, retrieval-augmented generation, contextual embeddings, vector databases, and semantic search tools Experience using deep learning frameworks and tools such as PyTorch, TensorFlow ...

Details • Salary: £80k–£100k base (flexible for strong profiles) • Working model: Hybrid, 3 days per week in London office • Tech stack: Python, JavaScript, GenAI (RAG, LangChain, LlamaIndex), agentic systems, Azure/AWS • Visa: No sponsorship available Interested? Please apply below. ...

writing functional user stories, and managing delivery cycles in an Agile environment. Direct experience shipping AI-powered tools or automated workflows (e.g., LLM integrations, RAG-based applications, or internal GPTs). Experience presenting to and influencing Board-level or Department-head stakeholders. A track record of measuring and proving ...

platform capabilities in a B2B SaaS environment, with a track record of shipping at enterprise scale. Deep understanding of agentic AI patterns, LLM orchestration, RAG, fine-tuning, and evaluation frameworks — and the ability to translate these into compelling, reliable product experiences. Experience managing the full ML lifecycle — development, training, evaluation ...

from operational data. Build scalable AI platforms and pipelines to support enterprise-grade model training, deployment and lifecycle management. Develop generative and retrieval-augmented AI systems leveraging LLMs, vector databases and modern AI tooling to unlock insights from complex technical datasets. Apply computer vision and sensor … Python expertise with modern ML frameworks including PyTorch, TensorFlow, Scikit-Learn, JAX, XGBoost, OpenCV and Transformers. Experience developing LLM and generative AI solutions, including RAG architectures, LangChain, vector databases and cloud ML deployment. Strong stakeholder communication skills Nice to haves: Experience in industrial/energy environments, enterprise AI platforms (Azure ...

intersection of software engineering , AI implementation , and agentic orchestration . You will work on: Multi‐agent architectures Intelligent tool and API integrations RAG pipelines and vector‐based retrieval Evaluation frameworks and AI observability Production workflows that ensure reliability, consistency, and scale You’ll play a critical role … systems trustworthy—handling failure modes, optimising agent collaboration, and improving robustness across diverse use cases. Key Responsibilities Build production AI systems using LLMs, RAG pipelines, vector databases, and agentic frameworks Design evaluation and observability frameworks to measure performance, accuracy, and reliability Develop clean, scalable applications with proper error handling, APIs ...

including APIs, services, and user-facing interfaces. Develop GenAI, Agentic AI and machine learning solutions that are scalable and production-ready. Design and implement RAG pipelines, LLM-powered applications and intelligent agents . Integrate AI solutions with enterprise platforms and cloud environments . Collaborate with data scientists, engineers and business … tuning and evaluation. Hands-on experience with GenAI frameworks such as LangChain, LlamaIndex and agent frameworks like AutoGen, CrewAI or LangGraph . Experience building RAG architectures with vector databases (Pinecone, Weaviate, FAISS). Strong understanding of MLOps/LLMOps (MLflow, Kubeflow, Docker, Kubernetes). Experience deploying AI solutions ...

senior stakeholders, with proven execution across complex migrations, AI enablement, and digital modernisation. Architect and deliver agentic AI systems (LangGraph, LangChain, tool-calling, memory), RAG pipelines, and enterprise ML workloads. Skilled in Azure and AWS AI stacks, Fast API microservices, Kubernetes, and secure vector search patterns. Strong background in FinOps … strategies. Regular contributor to AI leadership content, events and thought leadership, simplifying frontier AI for executive audiences. Core Capabilities Enterprise AI & Architecture Agentic AI, RAG, enterprise ML in regulated FS Azure AI Studio, Azure AI Search, Fabric, Document Intelligence AWS Bedrock & SageMaker pattern experience FastAPI, Kubernetes, Docker, CI/ ...

secure, scalable production systems. Lead the development and operationalisation of advanced models, including LLMs, diffusion models, and other generative techniques. Architect production-grade RAG systems, evaluation frameworks, and responsible AI guardrails. Stay at the forefront of AI research, translating emerging capabilities into practical, high-impact solutions. Mentor and lead cross … modern ML frameworks (PyTorch, TensorFlow). Strong experience with Generative AI tooling (e.g. LangChain, LangGraph or similar). Deep understanding of LLMs, prompt engineering, RAG, vector databases, evaluation, and security. Hands-on experience with fine-tuning, deploying, and monitoring large-scale GenAI systems. Strong MLOps/LLMOps capability: CI/ ...

choices directly shape what we ship to clients.Day to day, you'll work across the full delivery lifecycle: designing multi-agent orchestration pipelines and RAG patterns on the backend, building performant React frontends that surface AI-driven insights, and collaborating with customers, product owners, and infrastructure specialists to integrate generative … with generative AI components, ensuring high availability and resilience across Azure-hosted environments Architect complex backend pipelines in Python, incorporating multi-agent systems, advanced RAG patterns, and prompt engineering strategies within Azure’s enterprise AI ecosystem (Azure OpenAI, Azure AI Search, Document Intelligence) Establish and enforce best practices ...