Principal AI Architect and Enterprise AI Transformation Lead

Unissant delivers innovative capabilities to the agencies that keep our nation healthy and safe. We apply our domain expertise, data acumen, and technology know-how to achieve breakthrough results for our clients. Working collaboratively, we advance missions and careers through a focus on honesty, integrity, and dependability. We continuously look for talent excited to join that effort. To learn more about our exciting organization, please visit us at

We are seeking a Principal AI Architect and Enterprise AI Transformation Lead to join our team and support our federal client in Ashburn, VA.

The ideal candidate is a hands-on AI leader who combines deep technical expertise with enterprise architecture, AI strategy, and customer engagement. This individual will lead the adoption and operationalization of AI capabilities while personally shaping solution architecture, developing prototypes, and guiding secure deployment in customer environments.

The successful candidate will bring hands-on experience from a commercial AI company or comparable fast-moving commercial AI environment and apply those practices to secure federal missions. This individual will build and demonstrate AI solutions using Generative AI, Large Language Models, Agentic AI, Retrieval-Augmented Generation, machine learning, and cloud-native technologies, and must be equally comfortable writing code, designing enterprise architectures, leading customer demonstrations and workshops, guiding technical teams, and advising executives on AI strategy, adoption, and measurable mission value.

• Lead enterprise AI strategy, architecture, adoption, and roadmaps while translating priorities into executable initiatives across programs, customers, and technical teams.
• Identify high-value opportunities where AI can improve mission performance, decision-making, operational efficiency, automation, and customer services.
• Evaluate foundation models, AI platforms, development frameworks, and emerging technologies through hands-on experimentation and benchmarking, then recommend practical options based on mission fit, security, scalability, cost, and implementation risk.
• Rapidly design, build, test, and demonstrate AI and machine learning prototypes that validate high-value mission and business use cases and establish a path to production.
• Convert validated prototypes into secure, scalable, production-ready solutions using Generative AI, Large Language Models, Agentic AI, Retrieval-Augmented Generation, intelligent automation, and related enterprise AI technologies.
• Develop AI applications and services using Python, APIs, cloud-native services, and modern AI frameworks.
• Design and implement enterprise RAG solutions, including document ingestion, embeddings, vector databases, semantic search, grounding, retrieval, orchestration, and response evaluation.
• Design and develop AI agents and multi-agent workflows that integrate with enterprise applications, data sources, APIs, and business processes.
• Architect secure and scalable AI platforms across hybrid-cloud, multi-cloud, and on-premises environments.
• Design and implement MLOps and GenAIOps pipelines supporting model development, deployment, evaluation, monitoring, governance, and lifecycle management.
• Build and integrate data pipelines, vector databases, model endpoints, agent frameworks, APIs, and cloud-native AI services.
• Establish technical approaches for prompt engineering, model selection, fine-tuning, grounding, human-in-the-loop workflows, guardrails, and AI evaluation.
• Partner with engineering, cybersecurity, data, cloud, Dev Sec Ops , operations, and enterprise architecture teams to integrate AI capabilities with existing systems and target-state architectures.
• Conduct architecture reviews, code reviews, technical assessments, and solution evaluations to ensure alignment with security, privacy, governance, interoperability, performance, and scalability requirements.
• Define reusable AI reference architectures, development patterns, technical standards, and implementation best practices.
• Provide hands-on technical leadership by contributing to solution design and implementation, resolving complex technical issues, reviewing engineering work, and mentoring delivery teams.
• Lead customer demonstrations, technical workshops, discovery sessions, solution reviews, and executive briefings that translate AI capabilities into clear mission use cases, adoption paths, and measurable business value.
• Provide…