GovCIO is currently hiring fora highly experienced, hands-onaSenior Azure AI Solutions Architect / Engineerato lead the architecture, development, and implementation of enterprise Generative AI and intelligent document solutions within a complex federal application environment.

This individual will serve as a technical leader for the adoption of Microsoft Azure AI capabilities, working alongside existing application architects, developers, infrastructure teams, and subject matter experts to introduce secure, scalable AI capabilities into established enterprise systems.

Initial efforts will focus onaAI-assisted document intelligence, enterprise search and retrieval, Retrieval-Augmented Generation (RAG), document summarization, structured data extraction, document classification, and AI-assisted business process automation.

The successful candidate must be equally comfortableadesigning the architecture and building the solution. This is not solely an advisory or strategy position. The individual will be expected to configure Azure AI services, develop prototypes and production solutions, integrate with existing applications and data repositories, troubleshoot technical challenges, establish AI development patterns, and transfer knowledge to existing technical teams.

The position is remote and supports a federal customer in the Washington, D.C. metropolitan area.

• Lead the architecture, design, development, and implementation of enterprise Generative AI solutions using the Microsoft Azure AI ecosystem.
• Design and implement solutions usingaMicrosoft Foundry, Azure OpenAI/model services, Azure AI Search, Azure AI Document Intelligence, and related Azure capabilities.
• Architect and implementaRetrieval-Augmented Generation (RAG)asolutions over enterprise structured and unstructured data.
• Develop AI-assisted enterprise search capabilities capable of identifying relevant documents, pages, data elements, and supporting evidence across large document repositories.
• Develop capabilities for natural-language question answering, document summarization, information extraction, structured output generation, and AI-assisted research.
• Design and implement semantic, vector, hybrid, and agentic retrieval approaches as appropriate for specific business requirements.
• Develop secure integration patterns between Azure AI services and existing enterprise applications, APIs, databases, and document repositories.
• Integrate AI functionality into existing enterprise applications through REST APIs, SDKs, Java services, and other approved integration patterns.
• Evaluate and implementaAzure AI Document Intelligenceacapabilities for document classification, metadata/data extraction, confidence scoring, and exception handling.
• Design AI agents and orchestration workflows using Microsoft Foundry capabilities when appropriate.
• Partner with existing application,aIBM FileNet and IBM DataCapatechnical teams to introduce AI capabilities while leveraging existing enterprise platforms and investments.
• Design solutions that preserve application-level authorization and ensure users only receive AI-generated information derived from data they are authorized to access.
• Implement appropriate Azure identity, access management, secrets management, networking, logging, monitoring, and security controls.
• Establish AI evaluation and testing frameworks measuring retrieval accuracy, extraction accuracy, groundedness, hallucination rates, confidence, latency, performance, and business outcomes.
• Develop and execute technical proofs of concept and MVPs and transition successful capabilities into secure, scalable production implementations.
• Work with business and technical stakeholders to translate business processes and pain points into actionable AI solutions.
• Evaluate alternative AI technologies and integration approaches and provide technical recommendations based on feasibility, security, cost, scalability, and business value.
• Establish reusable architecture patterns, development standards, and technical documentation for future enterprise AI implementations.
• Mentor existing architects and developers on Azure AI, RAG, LLM integration, prompt engineering, evaluation, and responsible AI development.
• Provide hands-on knowledge transfer so that successful AI capabilities can ultimately be maintained and expanded by the existing technical organization.

Required Education and Experience

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, Artificial Intelligence, Engineering, or a related technical discipline with 12 years (or commensurate experience.
• Clearance Level: Public Trust

Required Skills & Experience

• 8+ years of progressive experiencedesigning, developing, and implementing enterprise software, cloud, or data solutions.
• 5+ years of experienceserving in senior engineering, technical architecture, cloud architecture, or comparable solution-design roles.
• 3+ years of hands-on experiencedesigning or implementing AI/ML solutions.
• 2+ years of recent hands-on experiencedesigning and implementing Generative AI, LLM, and/or RAG-based applications.
• Demonstrated hands-on experience with theaMicrosoft Azure AI ecosystem, including Microsoft Foundry/Azure AI Foundry and Azure-hosted foundation models.
• Demonstrated experience designing and implementingaRAG architecturesusing enterprise data.
• Hands-on experience withaAzure AI Searchor comparable enterprise semantic/vector search technologies.
• Strong understanding of:
  • Vector embeddings and vector databases/search
  • Semantic and hybrid search
  • Chunking and document-processing strategies
  • Prompt engineering and context management
  • Grounding and citation/source attribution
  • LLM orchestration
  • AI evaluation and hallucination mitigation
• Experience integrating LLMs and AI services with enterprise data sources and applications.
• Experience designing and implementing secureaREST API and SDK-based integrations.
• Experience withaPython and/or Javafor enterprise AI/application development.
• Experience with Microsoft Azure architecture, includingaEntra ID, RBAC, managed identities, Key Vault, networking, storage, monitoring, and logging.
• Demonstrated ability to design solutions involving sensitive or restricted enterprise information with appropriate data isolation and authorization controls.
• Demonstrated experience takingat least one enterprise AI/ML solution from architecture or proof-of-concept through production deployment.
• Experience establishing technical architecture while remaining directly involved in hands-on development and troubleshooting.
• Demonstrated ability to communicate complex AI architecture and technical concepts to both technical and non-technical stakeholders.
• Experience mentoring developers and transferring technical knowledge to existing engineering teams.

Preferred Skills & Experience

• Candidates located in the DC area are highly preferred
• Hands-on experience with Azure AI Document Intelligence, including document classification, custom extraction, structured data extraction, confidence scoring, and exception processing.
• Experience buildingaenterprise document intelligence, document search, or knowledge retrieval solutionsinvolving large volumes of unstructured content.
• Experience integrating AI solutions with enterprise content-management systems such asaIBM FileNet.
• Experience withaIBM DataCapor comparable enterprise document capture and processing platforms.
• Experience integrating AI functionality into existingaJava-based enterprise applications.
• Experience with Microsoft Foundry Agent Service and agentic AI architectures.
• Experience withaMicrosoft Copilot Studio, including agents, connectors, actions, and enterprise workflow integration.
• Experience with Azure DevOps, Git, CI/CD, Infrastructure as Code, and DevSecOps practices for AI-enabled applications.
• Experience implementing AI evaluation frameworks and automated testing for production LLM applications.
• Experience with document-level/page-level citations, source attribution, and explainable/traceable AI responses.
• Experience implementing human-in-the-loop workflows and confidence-based exception routing.
• Knowledge of responsible AI principles, model governance, data privacy, and AI risk management.
• Experience working with PII, financial, benefits, healthcare, or other regulated/sensitive information.
• Experience delivering solutions withinafederal government environments, including familiarity with FedRAMP, NIST controls, FISMA, ATO processes, and federal security requirements.
• Experience evaluating AI platforms, models, and services based on cost, performance, security, accuracy, and operational suitability.
• Experience with IBM watsonx or other enterprise AI platforms is beneficial but not required.