Senior Software Engineer/Developer - AI

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-scale modernization initiatives (e.g., legacy-to-cloud, microservices transformation, including Python-based refactoring and re-platforming efforts)
• Define repeatable modernization frameworks and accelerators
• Oversee DevSecOps pipelines, CI/CD automation, zero-trust architectures, and secure software supply chain practices
• Ensure delivery of resilient, high-availability systems in regulated federal environments
• Lead multiple concurrent engineering efforts across integrated teams
• Provide technical leadership to architects, engineers, and DevSecOps specialists, including establishing Python coding standards and engineering best practices
• Mentor senior engineers and technical leaders; elevate engineering excellence and code quality
• Support technical strategy in proposals, captures, and client engagements
• Contribute to thought leadership (whitepapers, architecture patterns, platform strategy)
• Expert-level proficiency in Python, including building large-scale AI/ML systems, APIs, and data pipelines
• Full-stack engineering skills, including front-end frameworks, back-end services, RESTful APIs, microservices, and cloud-native deployment (e.g., containers, Kubernetes)
• Deep expertise in machine learning and deep learning, particularly transformer-based models and LLMs
• Hands-on experience with ML frameworks (PyTorch, TensorFlow, JAX) and distributed training (DeepSpeed, FSDP, Horovod)
• Proven ability to integrate AI capabilities into existing and legacy enterprise systems (e.g., legacy CMS or COTS platforms) using APIs, middleware, connectors, and event-driven architectures
• Strong understanding of large-scale data systems and ML evaluation methodologies
• Experience working with sensitive data, including PII safeguards such as anonymization, masking, and data loss prevention
• Experience with enterprise integration technologies,