AI Solution Engineer
International financial institution · Washington, DC
- Location
- Washington, DC · Hybrid
- Salary band
- $65 – $65/hr
- Type
- Contract
- Level
- Mid
- Work authorization
- Not stated
Stack
Python · Azure OpenAI · Retrieval-Augmented Generation (RAG) · Model Context Protocol (MCP) · Azure AI Foundry · GitHub Copilot · Claude Code · Claude Skills · Microsoft Fabric · OneLake · Power BI · Knowledge Graph · Semantic Modeling · Ontology Design · Microsoft Entra ID · Microsoft Purview · Large Language Models · Agent-based Systems · Azure API Management · Responsible AI
About the role
AI Solution Engineer HYBRID in Washington, DC (Mandatory, no 100% remote permitted) Rate $65 hr W2 All Inc ID: 61403-1 Job Summary: Translate how [contact hidden] economists work into a practical solution design and ensure the Cognitive Digital Twin produces results that are transparent, traceable, and defensible. 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. Required Experience • 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. • Ability to work onsite in Washington, DC using [contact hidden]-managed technology and development environments. 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. • 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.
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