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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Mid-Senior AI Engineer - **Company:** ReNude - **Location:** Glasgow, UK (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Django Web Framework, Graph Database, Python (Programming Language), Machine Learning, Natural Language Processing, Neo4j, Recommender Systems, Search Technologies, Management of Software Versions, Web Applications, AI Infrastructure, Chatbots, ReactJS, Large Language Models, Multi-Agent Systems, Backend, Fastapi, AI Platforms, Information Technology, Low Latency, Machine Learning Operations, Docker - **Published:** May 17, 2026 - **Apply:** https://find.jobs/jobs-near-me/mid-senior-ai-engineer-glasgow/2774757614-2/ ## About the Role We're hiring a mid-senior (4+ years experience) AI Engineer to help design, build, and scale our production AI systems with ownership from design to deployment. You'll work end-to-end across Agentic RAG conversational agents, Agent orchestration, fine-tuning models, retrieval pipelines, evaluation frameworks and generating product recommendation for real customer interactions to deliver reliable AI-powered experiences in production., * Bachelor's degree in Computer Science, Artificial Intelligence, Engineering, or equivalent practical experience * 4+ years experience building and deploying production AI applications * 2+ years experience developing LLM-powered products, conversational AI, RAG systems, or agentic workflows * Strong Python skills and experience building backend systems and APIs * Hands on experience building agentic workflows, tool-calling pipelines, and stateful orchestration using frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI or similar agent orchestration frameworks * Strong experience designing and optimising retrieval systems, including vector stores (Pinecone, Weaviate, Chroma), graph-based RAG architectures, hybrid retrieval, reranking, and semantic search pipelines * Experience with production optimisation for RAG systems, including latency optimisation, token cost control, context management, monitoring, guardrails and prompt versioning * Exposure to AIOps/MLOps practices and hands-on experience with AWS Bedrock, SageMaker, Vertex AI, or similar cloud AI platforms * Comfortable working autonomously and taking ownership in a fast-moving, remote, startup environment Nice-to-Haves: * Masters or advanced degree in artificial intelligence, machine learning, natural language processing or related field * Experience with LLM fine-tuning techniques such as LoRA, QLoRA, supervised fine-tuning (SFT), instruction tuning, and parameter-efficient model adaptation workflows * Experience with evaluation and monitoring frameworks for LLM systems, including RAGAS, OpenEvals, DeepEval, LangSmith, OpenTelemetry or LLM-as-a-Judge evaluation frameworks * Familiarity with graph databases such as Neo4j or Amazon Neptune * Experience with recommendation systems, personalisation pipelines or ranking systems * Experience building modern web applications using Django, FastAPI, React, or similar frameworks * Comfortable using Docker and containerised development workflows * Exposure to CI/CD pipelines, infrastructure-as-code, and cloud-native deployment practices ## Description * Design, build, and deploy Agentic AI workflows and RAG-powered applications for real-world use cases * Develop scalable backend services and APIs powering AI agents and conversational systems * Build and optimise retrieval pipelines including embeddings, chunking, reranking, memory and vector search * Collaborate closely with product, design, and engineering teams to shape AI-first experiences * Integrate LLM systems into user-facing web applications and product features * Improve response quality, reliability, latency, observability and cost efficiency of production AI systems * Evaluate new AI frameworks, tools, and orchestration approaches across the rapidly evolving LLM ecosystem * Influence architecture decisions for scalable, secure, and production-ready AI infrastructure ## Related Videos - [RAG's Not Dead, You're Just Using It Wrong! - Phil Nash](https://www.wearedevelopers.com/videos/1906-rag-s-not-dead-you-re-just-using-it-wrong-phil-nash) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [On a Secret Mission: Developing AI Agents](https://www.wearedevelopers.com/videos/1510-on-a-secret-mission-developing-ai-agents) - [Building and Deploying Multi-Agent Systems with ADK and Vertex AI](https://www.wearedevelopers.com/videos/1918-building-and-deploying-multi-agent-systems-with-adk-and-vertex-ai) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care)