Principal AI Software Developer

Overview

The Senior Principal Software Engineer/Developer will serve as a lead enterprise architect and AI engineering authority, responsible for defining and delivering large-scale, mission-critical AI-enabled systems for HUD and the AIR platform. This role goes beyond hands-on development to include enterprise-wide technical strategy, cross-program architecture leadership, executive-level stakeholder engagement, and AI governance and innovation leadership in federal environments.

Responsibilities

• Serve as the primary technical authority across multiple programs, defining AI and application architecture strategy at the enterprise level 

• Establish long-term modernization roadmaps for federal systems, aligning AI adoption with mission outcomes and compliance requirements  

• Act as a trusted advisor to senior federal stakeholders (SES-level and above) on AI strategy, system modernization, and technology risk  

• Drive technical decision-making across portfolios, ensuring scalability, interoperability, and long-term sustainability 

• Own end-to-end enterprise architecture across multiple interconnected systems, platforms, and programs  

• Lead design of complex, distributed AI-enabled architectures, including multi-cloud and hybrid environments  

• Define and enforce architecture standards, reference architectures, and reusable frameworks across teams 

• Lead development of advanced AI solutions, including RAG systems at scale, agentic workflows and orchestration frameworks, and AI-driven automation across mission workflows 

• Establish AI engineering best practices and standards across the organization  

• Evaluate and lead adoption of emerging AI technologies, guiding pilots into production-ready capabilities  

• Drive innovation initiatives tied to AIR platform growth and differentiation 

• Oversee multiple concurrent development efforts, ensuring alignment with architecture, security, and delivery goals  

• Provide technical leadership across integrated product teams, including architects, engineers, and DevSecOps specialists  

• Ensure delivery of high-availability, production-grade systems supporting federal missions 

• Serve as a subject matter expert in federal AI policy and governance, including NIST AI RMF, OMB M-24-10, and EO 14110 

• Establish and oversee organization-wide responsible AI frameworks, including model evaluation and validation, bias mitigation strategies, and explainability and auditability 

• Ensure full compliance across programs with FISMA, FedRAMP, NIST 800-53, privacy, accessibility (Section 508), and security mandates 

• Lead large-scale legacy modernization initiatives, including enterprise COBOL-to-Python transformations, migration to cloud-native, and microservices-based architectures 

• Define repeatable modernization frameworks and accelerators 

• Oversee implementation of enterprise-grade DevSecOps pipelines and platform engineering practices 

• Drive adoption of, advanced CI/CD automation, Zero-trust architectures, and secure supply chain practices (SBOM, scanning, policy enforcement) 

• Mentor senior engineers, architects, and technical leads  

• Build and scale high-performing engineering teams  

• Lead technical hiring, workforce planning, and capability development 

• Play a key leadership role in proposals, captures, and recompetes

• Serve as technical lead in client presentations, demos, and strategy sessions  

• Contribute to thought leadership (whitepapers, architecture frameworks, innovation roadmaps) 

Qualifications

Knowledge/Skills/Abilities: Initial:   

• Proven ability to operate as an Enterprise architect, chief engineer, or technical director-level leader 

• Strong executive communication skills with demonstrated success influencing C-suite or federal senior leadership  

• Deep expertise in scaling engineering organizations and delivering across complex programs  

• Ability to balance strategic vision with hands-on technical depth 

 

Qualifications: 

• US Citizenship is required 

• Bachelor's or master's degree in Computer Science, Software Engineering, or a related field 

• 12–15+ years of software engineering experience, including 6–8+ years in AI-enabled systems (LLMs, generative AI, applied ML in production) (enhanced from 8+ years to