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 1000-53 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, including REST/SOAP services, message queues, ETL pipelines, and SQL/NoSQL databases Expertise designing AI systems in cloud-native, distributed environments across AWS, Azure, and GCP Proficiency with managed generative AI services (e.g., AWS Bedrock, Azure OpenAI Service, Google Vertex AI) and integrating frontier models such as GPT, Claude, and Gemini Hand