Microsoft AI Engineer

The Opportunity This is a unique opportunity to help establish and shape a new AI capability within a complex, multi-functional organisation. Working in a greenfield environment, you will play a key role in defining architectural standards, engineering practices, governance frameworks and delivery approaches that will underpin future AI initiatives. The role offers significant autonomy and ownership, with responsibility for transforming AI opportunities into practical business solutions. You will work closely with business stakeholders and analysts to identify high-value use cases, rapidly develop proofs of concept, and scale successful solutions into production. This is a foundations role focused on creating scalable, sustainable AI capabilities rather than maintaining legacy systems. The decisions made in the early stages will influence how AI solutions are designed, governed, evaluated and deployed across the organisation in the future. One of the founding members of a newly established AI function. Initially operating as the primary hands-on AI engineering resource, with technical mentorship from the Automation Manager. The function is expected to grow as successful use cases are scaled and organisational AI adoption increases. The Role The AI Engineer will play a central role in designing, building and operating AI-powered solutions that improve productivity, decision-making, customer experience and operational effectiveness. This is a highly hands-on position requiring ownership of the end-to-end delivery lifecycle, from solution architecture and platform selection through to development, testing, deployment and handover. The successful candidate will work with stakeholders across business functions, technology teams and external partners to translate opportunities into production-ready AI products and automations. The role combines software engineering, AI engineering, automation and solution architecture. You will design and build AI agents, orchestration workflows and intelligent automation solutions whilst ensuring they are secure, scalable, maintainable and aligned with business objectives. While supported by technical leadership, the AI Engineer will be expected to operate independently, exercising sound judgement and taking ownership of architectural and implementation decisions. Key Responsibilities Discovery and Proof of Concept 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. Delivering Production Solutions 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. Platform, Architecture and Governance 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. Skills and Experience Essential 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. Desirable 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. TPBN1_UKTJ