AI Enablement/ Business Applications Engineer

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Job Summary
The AI Solutions Engineer is responsible for designing and delivering practical AI-enabled tools that directly support business operations. The role focuses on turning business requirements into usable AI solutions that automate workflows, reduce manual effort, and improve decision-making across engineering, estimating, and reporting functions. Success in this role is measured by working tools delivered, adoption by users, and measurable productivity improvement.
Key Responsibilities
1. Business Requirement Analysis & AI Use Case Definition:
Activities involving understanding business needs and translating them into AI‐enabled solutions:
• Work closely with stakeholders (Engineering, Estimating, Operations, Management) to identify high-impact use cases suitable for AI adoption.
• Analyze current workflows and pain points to define clear functional and technical requirements.
• Propose AI solutions aligned with business value, feasibility, and data availability.
• Prioritize initiatives based on ROI, scalability, and operational impact.
2. AI Solution Design & Development:
• Design, develop, and deploy custom AI agents using Microsoft Copilot Studio, Microsoft 365 Copilot, and Azure AI services to support real business workflows.
• Build and maintain AI‐powered tools integrated with Microsoft 365 applications, including Teams, Outlook, SharePoint, Excel, Word, and the Power Platform, to improve productivity and information flow
• Implement and optimize prompt engineering, conversation design, and orchestration logic to ensure accurate, reliable, and business‐ready AI outputs.
• Design scalable conversation flows and agent behaviors aligned with business rules, data context, and user experience.
• Validate AI agent performance with end users and continuously refine prompts, logic, and integrations based on feedback and observed outcomes.
3. User Enablement & Collaboration:
• Act as a trusted advisor to business teams on how AI can support their daily work.
• Train users on adopted AI tools, including proper usage and interpretation of outputs.
• Gather user feedback and continuously refine tools to better meet operational needs.
• Collaborate with engineering, IT, and management teams to ensure alignment with company strategy.
4. Quality, Governance & Tooling:
• Ensure AI solutions follow company standards for quality, security, and documentation.
• Maintain clear documentation for AI tools, workflows, prompts, and data dependencies.
• Support governance practices to ensure responsible and ethical use of AI within the organization.
5. Continuous Improvement & Innovation:
Activities improving processes, tools, and ways of working:
• Proactively research emerging AI technologies and identify practical applications relevant to the business.
• Drive continuous improvement initiatives by proposing enhancements to existing AI tools and workflows.
• Share lessons learned, best practices, and success stories across the organization.
• Support management in shaping the long‐term AI roadmap for the company.