Microsoft AI Engineer

Salary: £60,000 - 60,000 per year

Requirements:

• 2-6 years experience in AI engineering, software engineering, automation engineering or a related technical discipline.
• Demonstrable experience delivering production solutions, supported by a portfolio or real-world examples.
• Strong interest in AI, automation and emerging technologies, with evidence of continuous learning and experimentation.
• Experience making architectural and technical decisions independently.
• Hands-on experience building and deploying AI solutions using modern AI platforms and large language models.
• Strong Python development skills, including testing, version control and CI/CD practices.
• Experience working with cloud platforms and cloud-native deployments.
• Practical understanding of prompt engineering, retrieval-augmented generation (RAG), agent orchestration and AI evaluation techniques.
• Experience integrating APIs, data platforms and enterprise systems.
• Excellent analytical, problem-solving and troubleshooting skills.
• Experience with Microsoft Copilot Studio, Azure AI Foundry, Power Platform and related technologies.
• Strong written and verbal communication skills, with the ability to clearly document technical solutions and explain trade-offs to both technical and non-technical stakeholders.
• Experience designing and implementing AI agents, copilots and intelligent automation solutions.
• Knowledge of Azure Functions, Azure AI Search, Semantic Kernel, Microsoft Agent Framework or similar technologies.
• Experience with data modelling, governance, security and enterprise architecture.
• Exposure to machine learning, predictive analytics or forecasting solutions.
• Relevant Microsoft or cloud platform certifications, such as AI-102, AI-103, AB-620 or equivalent.

Responsibilities:

• Partner with business stakeholders and analysts to identify high-value AI and automation opportunities.
• Evaluate processes and workflows to determine where AI can deliver measurable business value.
• Rapidly develop proofs of concept to validate technical feasibility and business outcomes.
• Assess and recommend the most appropriate technologies, platforms and approaches for each use case.
• Contribute to documentation, governance frameworks and knowledge repositories to support responsible AI adoption.
• Progress successful proofs of concept into robust, production-grade AI solutions.
• Design and build AI agents, copilots and automation workflows using appropriate low-code and pro-code platforms.
• Develop and maintain Python-based services, integrations and orchestration components.
• Build and support workflow automations, including error handling, monitoring and operational resilience.
• Integrate enterprise systems, data sources, APIs and external services while maintaining security and compliance standards.
• Perform testing, validation and performance optimisation to ensure reliable production operation.
• Own and contribute to the architecture of the AI solution landscape.
• Select appropriate knowledge, retrieval and data-access strategies for different AI use cases.
• Implement evaluation frameworks, guardrails, monitoring and observability for AI solutions.
• Ensure solutions are developed in line with security, privacy, compliance and data governance requirements.
• Establish deployment standards, CI/CD practices and reusable engineering patterns.
• Maintain high-quality documentation to support knowledge transfer and future team growth.
• Design solutions with cost efficiency and measurable return on investment in mind.

Technologies:

• AI
• AI Agents
• Azure
• CI/CD
• Cloud
• Copilot
• Support
• Machine Learning
• Python
• RAG
• Security

More:

We are building a new AI capability within a complex, multi-functional organisation and are looking for one of the founding members of this function. This is a greenfield opportunity with significant autonomy and ownership, initially as the primary hands-on AI engineering resource with technical mentorship from our Automation Manager. Our goal is to create scalable, sustainable AI capabilities that transform opportunities into practical business solutions and shape how AI is designed, governed, evaluated and deployed across the organisation. The function is expected to grow as successful use cases are scaled and AI adoption increases.

last updated 32 week of 2026