AI Solution Engineer

Job Summary

Key Responsibilities

* Work directly with SPR economists to understand how survey analysis is performed today and identify which activities can be automated, which require analyst review, and where AI can assist decision-making.

* Document business requirements, user journeys, and solution specifications that guide engineering and AI development teams.

* Define and maintain role-based prompts, agent instructions, and approved interaction patterns.

* Establish and enforce evidence standards so every output can be traced back to its source survey, reporting period, methodology, and underlying calculations.

* Lead the design of the project’s knowledge graph and semantic model, ensuring relationships between surveys, indicators, countries, methodologies, institutions, and analytical concepts are consistently represented and reusable across agents.

* Define business use cases for graph-based reasoning, including cross-country comparisons, indicator lineage, concept discovery, policy-link analysis, and expert knowledge retrieval.

* Own business validation criteria, benchmark scenarios, and “golden questions” used to determine whether the solution is ready to progress beyond prototyping.

* Govern the lifecycle of agent assets, including instructions, skills, knowledge sources, evaluation criteria, and governance controls.

* Review AI-generated code, configurations, and solution artifacts as the accountable business and architecture reviewer.

* Prepare architecture, security, governance, and review materials for EARB and other approval bodies.

* Partner with data architects and engineers to ensure the knowledge graph, retrieval mechanisms, and analytical models align with business expectations and governance requirements.

* Design and support agent-to-UI and application integrations using AG-UI, Vercel AI SDK/UI, and Mastra.

* Define and implement integration patterns between AI agents, enterprise applications, APIs, data platforms, knowledge sources, and external services.

* Establish AI evaluation and observability practices, including tracing, monitoring, performance assessment, debugging, and troubleshooting.

Required Experience

* Minimum 8+ years of overall IT experience required.

* 3+ years delivering data, analytics, or AI solutions.

* 2+ years working with large language models, AI assistants, or agent-based systems.

* Experience translating business processes into functional and technical specifications.

* Demonstrated ability to work with researchers, economists, analysts, or other subject matter experts.

* Strong facilitation and stakeholder engagement skills.

Required Technologies

* Python, with the ability to review and validate data-processing and AI-integration code.

* Azure OpenAI and Retrieval-Augmented Generation (RAG) architectures, including grounding, retrieval strategies, and Model Context Protocol (MCP).

* Azure AI Foundry evaluation capabilities and model assessment practices.

* GitHub Copilot, agent specifications, instruction libraries, and AI-assisted development practices.

* Claude Code and Claude Skills, including authoring and governance of instructions, skills, and behavioral guardrails.

* AG-UI, Vercel AI SDK/UI, and Mastra, including agent/application integration patterns.

* AI evaluation and observability, including agent tracing, monitoring, debugging, and performance assessment.

* API and enterprise system integrations for connecting agents with applications, data sources, tools, and services.

* Microsoft Fabric, OneLake, and Power BI, including architectural decision-making for enterprise data platforms.

* Knowledge graph technologies, semantic modeling, ontology design, metadata management, and graph-based retrieval patterns.

* Microsoft Entra ID and role-based access control.

* Microsoft Purview or equivalent solutions for cataloging, lineage, governance, and auditability.

Preferred

* Azure API Management and Azure Monitor/Application Insights.

* Responsible AI frameworks, content safety controls, and red-team testing practices.

* Experience designing enterprise knowledge graphs and semantic layers for search, discovery, recommendation, and AI grounding.

* Experience with Google Cloud AI services, including Vertex AI, Gemini, Agent Development Kit (ADK), and BigQuery, demonstrating cross-platform understanding of enterprise AI architectures.