Overview
The Senior Principal Software Engineer/Developer – AI serves as a senior, hands-on full-stack AI engineer and technical authority, leading the technical strategy, design, and delivery of large-scale mission-critical AI systems supporting federal programs (e.g., HUD, AIR platform). This role combines senior technical leadership, hands-on expertise in Python-based AI/ML systems (including large language models), and ownership of enterprise architecture, governance, and innovation.
Responsibilities
- Serve as the primary technical authority, defining AI and application architecture across multiple programs
- Establish enterprise modernization roadmaps aligned to mission outcomes, compliance, and scalability
- Lead architecture for distributed, cloud-native, and hybrid AI systems
- Define and enforce reference architectures, standards, and reusable frameworks
- Drive cross-program technical decision-making to ensure interoperability, security, and long-term sustainability
- Advise senior federal stakeholders (SES-level and above) on AI adoption, modernization, and risk management
- Lead design, development, and deployment of advanced AI solutions using Python as the primary development language, including large language models (LLMs) and foundation models, Retrieval-Augmented Generation (RAG) systems, agentic workflows, and orchestration frameworks
- Architect and implement scalable ML systems and services built on Python-based frameworks and APIs
- Build full-stack AI applications end to end, from user-facing interfaces to back-end services, APIs, and data layers
- Integrate AI and LLM capabilities into existing enterprise applications and legacy platforms (e.g., content management, case management, and records systems) via APIs, middleware, and event-driven patterns
- Define and implement distributed training strategies (GPU/TPU clusters, parallelization, optimization)
- Oversee full ML lifecycle in partnership with the Senior Data Scientist: data pipelines, feature engineering, training, evaluation, deployment, and monitoring
- Drive model optimization techniques (quantization, distillation, caching) to improve performance and cost
- Establish robust MLOps practices leveraging Python-driven automation, pipelines, and tooling
- Stand up the enterprise CI/CD-to-AI/MLOps pipeline, beginning with time-boxed proofs of concept and MVP implementations that mature into production systems
- Serve as subject matter expert in federal AI policy (e.g., NIST AI RMF, OMB M-25-21 and M-25-22, Executive Order 14179)
- Define and operationalize Responsible AI frameworks, including model validation and evaluation, bias mitigation and fairness, and explainability, auditability, and safety
- Ensure compliance with FISMA, FedRAMP, NIST 800-53, privacy, and Section 508 requirements
- Lead large-sca