AI Copilot engineer in Warwick

The Role

• Role Purpose

• The AI Generalist is responsible for designing, implementing, deploying and governing AI solutions across the organisation, leveraging Generative AI, Agentic AI architectures, Azure AI Services and modern AI engineering practices. The role combines business understanding, solution architecture, AI engineering, data literacy and change leadership to identify opportunities, rapidly develop AI-powered capabilities, and scale them into secure, production-grade services.

• The role will lead the adoption of AI technologies whilst ensuring solutions remain observable, secure, reliable, explainable and aligned with Responsible AI principles.

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• Key Responsibilities

• Agentic AI Solution Development

• Design and implement AI agents capable of autonomous task execution across business processes.

• Develop multi-agent and agent orchestration architectures to automate complex workflows.

• Build solutions using Retrieval Augmented Generation (RAG), tool calling, memory frameworks and workflow orchestration patterns.

• Evaluate and implement emerging agentic frameworks and architectures.

• Define governance controls for autonomous AI agents.

• AI Platform Engineering & Azure AI

• Design and deploy AI solutions using Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning and Azure AI Services.

• Configure secure AI integrations with enterprise applications and data platforms.

• Collaborate with cloud, security and data teams to establish scalable AI platforms.

• Support deployment pipelines, model lifecycle management and operational readiness activities.

• AI Operations, Monitoring & Observability

• Implement observability frameworks for AI solutions, including performance, reliability and usage monitoring.

• Establish monitoring for prompts, tokens, latency, hallucinations, model drift and agent execution.

• Define KPIs and telemetry requirements for AI services.

• Support incident investigation, root cause analysis and continuous service improvement.

• Ensure AI services meet operational support and availability requirements.

• Responsible AI & Governance

• Establish guardrails for AI applications and autonomous agents.

• Ensure compliance with Responsible AI policies, security requirements and emerging regulations.

• Conduct risk assessments for AI-enabled business processes.

• Develop governance standards and operational controls for AI solutions.

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• Essential Skills & Experience

• AI Engineering

• Proven experience delivering AI solutions in enterprise environments.

• Strong understanding of Large Language Models (LLMs), Generative AI and Agentic AI architectures.

• Experience implementing Retrieval Augmented Generation (RAG) solutions.

• Experience designing AI workflows incorporating tool calling, orchestration and memory patterns.

• Experience evaluating, deploying and operating production AI solutions.

• Azure AI (Essential)

• Hands-on experience with Azure AI Services.

• Experience using Azure OpenAI.

• Experience with Azure AI Search.

• Experience with Azure AI Foundry.

• Experience with Azure Machine Learning.

• Understanding of Azure-based AI security, networking and governance controls.

• Observability & Monitoring (Essential)

• Experience implementing monitoring and observability for AI and digital platforms.

• Knowledge of application monitoring and telemetry solutions.

• Ability to define operational metrics, dashboards and alerts.

• Experience investigating AI performance issues and identifying optimisation opportunities.

• Understanding of model monitoring, drift detection and AI operational support processes.

• Technical Skills

• Python development.

• REST APIs and integrations.

• Vector databases and semantic search.

• Prompt engineering.

• AI workflow automation.

• Data modelling and data integration principles.

• Cloud-native architecture principles.

• Git-based development and DevOps practices.

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• Technical Knowledge

• Azure OpenAI

• Azure AI Foundry

• Azure AI Search

• Azure Machine Learning

• Microsoft Copilot Studio

• Semantic Kernel

• LangChain / LangGraph

• AutoGen

• Vector databases

• Azure Monitor

• Application Insights

• Datadog / Dynatrace / New Relic

• Power Platform

• Microsoft Fabric

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• Success Measures

• Within 6 months the AI Generalist will:

• Deliver two production-grade AI solutions.

• Implement at least one enterprise agentic AI capability.

• Establish AI observability and monitoring standards.

• Improve business productivity through AI-enabled automation.

• Create reusable patterns for RAG and Agentic AI deployments.

• Ensure all AI solutions operate within agreed governance and Responsible AI frameworks.