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Based on the provided job description and HTML structure, here's a clear, concise job summary for McGregor Boyall's AI Engineer role: --- **AI Engineer - Enterprise Knowledge Base (EKB)** McGregor Boyall is seeking an experienced AI Engineer to design, build, and deploy enterprise-grade AI solutions that enhance knowledge discovery, retrieval, and automation. This role requires strong expertise in Generative AI, Large Language Models (LLMs), AI Agents, and modern AI application frameworks, with a passion for delivering scalable, production-ready solutions. **Responsibilities:** - Design and develop AI-powered applications using Generative AI, LLMs, NLP, and Agentic AI technologies. - Build intelligent RAG (Retrieval-Augmented Generation) solutions that leverage embeddings and vector databases. - Develop and orchestrate AI Agents and Multi-Agent Systems to automate complex business workflows. - Apply prompt engineering and context engineering techniques to optimize AI performance and accuracy. - Implement AI solutions using frameworks such as LangChain, LangGraph, and MPAI. - Integrate AI services and enterprise platforms through REST APIs and cloud-native architectures. - Deliver scalable, secure, and reliable solutions using Python, SQL, Git, and CI/CD practices. - Test, evaluate, and continuously improve LLM and AI Agent performance, reliability, and safety. - Collaborate with business, data, and engineering teams to drive AI adoption and innovation. **Required Skills:** - Strong proficiency in AI, Generative AI (GenAI), LLMs, and NLP. - Prompt engineering, context engineering, AI agents, agentic AI, and multi-agent systems. - Knowledge of frameworks such as LangChain, LangGraph, and MPAI. -
- Compensation
- £800 – £800 GBP
- City
- Manchester
- Country
- United Kingdom
Currency: £ (GBP)
Full Job Description
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Job Details
AI Engineer
- 800.00 per day
- manchester, Manchester, M2 4PD, England, United Kingdom
- Contract
Role Overview
We are looking for an experienced AI Engineer to design, build, and deploy enterprise-grade AI solutions that enhance knowledge discovery, retrieval, and automation. The ideal candidate will have strong expertise in Generative AI, Large Language Models (LLMs), AI Agents, and modern AI application frameworks, with a passion for delivering scalable, 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 business workflows.
- Apply Prompt Engineering and Context Engineering techniques to optimise AI performance and accuracy.
- Implement AI solutions using frameworks such as LangChain, LangGraph, and MCP.
- Integrate AI services and enterprise platforms through REST APIs and cloud-native architectures.
- Deliver scalable, secure, and reliable solutions using Python, SQL, Git, and CI/CD practices.
- Test, evaluate, and continuously improve LLM and AI Agent performance, reliability, and safety.
- Collaborate with business, data, and engineering teams to drive AI adoption and innovation.
- AI, Generative AI (GenAI), Large 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
- Knowledge Graphs
- Semantic Search
- Responsible AI
- Token Optimisation & Cost Management
- AI Evaluations (Evals)
- Re-ranking Techniques
- Caching Strategies
- Experience designing and implementing Enterprise Knowledge Bases (EKBs), AI-powered enterprise search, knowledge management, or intelligent retrieval platforms.
- Experience delivering AI solutions from prototyping through to production deployment in enterprise environments.
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