AI Engineer

McGregor Recruitment; Manchester; On-site; GBP 70,000 - 110,000; Full time

Job summary

McGregor Recruitment is seeking an experienced AI Engineer to design, build, and deploy enterprise-grade AI solutions that enhance knowledge discovery, retrieval, and automation.

You will work with GenAI, LLMs, NLP, and AI Agents, delivering scalable production systems and collaborating with business and engineering teams to drive AI adoption.

Qualifications

• Proficiency in GenAI, LLMs, NLP and Agentic AI.
• Experience with LangChain, LangGraph and MCP.
• Knowledge of RAG, embeddings and vector databases.
• Strong Python, SQL, Git and CI/CD practices.
• Experience with cloud platforms (Azure, AWS or GCP).
• LLM and AI agent testing and evaluation.

Responsibilities

• Design, build and deploy GenAI-powered AI solutions.
• Develop RAG solutions with embeddings and vector databases.
• Create AI agents and multi-agent systems to automate workflows.
• Apply prompt and context engineering to optimize performance.
• Integrate AI services via REST APIs and cloud architectures.
• Collaborate with business, data and engineering teams.

Skills

AI

Generative AI

Large Language Models

NLP

Prompt Engineering

Context Engineering

AI Agents

Agentic AI

Multi-Agent Systems

LangChain

LangGraph

MCP

RAG

Embeddings

Vector Databases

Python

SQL

REST APIs

Git

CI/CD

Cloud Platforms

Azure

AWS

GCP

LLM Testing

AI Agent Testing

Job description

AI Engineer – Enterprise Knowledge Base (EKB)

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.

Required Skills

• 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

Desirable Skills

• Knowledge Graphs
• Semantic Search
• Responsible AI
• Token Optimisation & Cost Management
• AI Evaluations (Evals)
• Re-ranking Techniques
• Caching Strategies

Preferred Experience

• 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.