All hired employees are expected to have experience with Microsoft Copilot and / or an approved equivalent AI solution.
Position Purpose
Build practical, secure AI and automation solutions that move from business or mission need through deployment, adoption, and measurable operational value.
Position Summary
The Senior AI Solutions Engineer will be part of the DSA Technology & Innovation Office (TIO) and work directly with DSA departments and Defense, Intelligence Community (IC), and Federal Civilian customer teams to identify, design, build, and deploy AI agents, intelligent automation, and agentic workflows that improve mission and business outcomes. This is a hands‑on engineering role that bridges business and mission needs with DSA's AI and Digital Transformation capabilities. The Senior AI Solutions Engineer will work with stakeholders to understand processes and pain points, rapidly prototype solutions, integrate AI with enterprise systems and data, and move successful solutions into secure, governed, operational use.
Key Responsibilities
AI Discovery and Solution Design
- Partner with DSA departments and customer teams to identify high-value opportunities for AI and automation and understand current processes, pain points, data, systems, and operational constraints.
- Evaluate and prioritize use cases based on mission or business value, feasibility, security, data availability, implementation effort, and risk.
- Translate business and mission needs into solution designs, technical requirements, prototypes, backlog items, implementation plans, and measurable success criteria.
- Identify opportunities to reuse solutions, patterns, connectors, agents, and components across departments and customer environments.
Agent and Automation Development
- Design, build, configure, test, and deploy AI agents and agentic workflows using Microsoft Copilot Studio, Power Automate, Power Apps, SharePoint, and other approved AI and automation technologies.
- Develop agent instructions, knowledge grounding, retrieval approaches, tool integrations, custom connectors, and human-in-the-loop workflows.
- Integrate AI capabilities with enterprise applications, APIs, data sources, and operational systems.
- Rapidly prototype and demonstrate solutions with stakeholders, using feedback to iteratively improve functionality and usability.
- Develop maintainable and reusable solution components where practical to accelerate future AI implementations.
Testing, Governance, and Security
- Develop evaluation criteria and test sets to validate agent accuracy, reliability, security, usability, and expected outcomes before deployment and following significant changes.
- Implement responsible AI practices, including appropriate human oversight, data protection, identity and access controls, auditability, and governance.
- Work with IT, cybersecurity, data owners, and customer stakeholders to ensure solutions operate within authorized environments and approved data boundaries.
- Identify technical, security, operational, and adoption risks and incorporate appropriate mitigations into solution designs.
Deployment, Adoption, and Continuous Improvement
- Support deployment and transition of successful prototypes into sustainable operational solutions.
- Develop user guidance, demonstrations, training, and other enablement materials needed to drive adoption.
- Monitor user feedback, adoption, solution performance, and business or mission outcomes following deployment.
- Continuously refine deployed solutions based on operational use, changing requirements, and technology capabilities.
- Help quantify productivity improvements, cost avoidance, cycle‑time reduction, quality improvements, or other measurable outcomes generated by AI solutions.
Innovation and Technology Evaluation
- Evaluate emerging AI, agentic, automation, and integration technologies through practical experimentation and prototyping.
- Recommend technologies and approaches that have a clear business or mission application rather than adopting technology for its own sake.
- Contribute reusable patterns, lessons learned, demonstrations, and technical assets to DSA's broader AI and Digital Transformation capabilities.
- Support internal demonstrations and customer-facing discussions that showcase practical applications of DSA's AI capabilities.
Required Qualifications
Bachelor's degree in Computer Science, Information Systems, Data Science, Artificial Intelligence, Engineering, or a related technical discipline, or an equivalent combination of education and relevant pra