Principal AI Software Developer

Overview

The Senior Principal Full Stack AI Engineer serves as a senior, hands-on full-stack AI engineer, leading the design, development, and delivery of large-scale mission-critical AI systems supporting the AIR Platform. This role combines senior technical leadership, hands-on expertise in AI and LLM systems built on a modern cloud stack (Next.js, Terraform, Git Hub, AWS, Azure, and GCP), and ownership of enterprise architecture, governance, and innovation.

This is a full-time position under Nexus for Pyramid Systems.

• Serve as a senior technical lead, defining AI and application architecture for the AIR Platform in partnership with and under the direction of the Director
• 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
• Lead design, development, and deployment of advanced AI solutions, including large language models (LLMs) and foundation models, Retrieval-Augmented Generation (RAG) systems, agentic workflows, and orchestration frameworks
• Architect and implement scalable AI applications and services using Next.js, cloud-native APIs, and managed AI services across AWS, Azure, and GCP
• 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
• Oversee the full AI solution lifecycle: data pipelines, evaluation, deployment, and monitoring
• Drive LLM performance and cost optimization (e.g., caching, prompt and context optimization, model selection)
• Establish robust MLOps practices leveraging Git Hub-based automation, CI/CD 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 refactoring and re-platforming efforts)
• Define repeatable modernization frameworks and accelerators
• Oversee Dev Sec Ops  pipelines and CI/CD automation (e.g., Git Hub Actions), 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 Dev Sec Ops  specialists, including establishing 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 across the platform stack (Next.js, Terraform, Git Hub, AWS, Azure, and GCP), including building large-scale AI applications, APIs, and data pipelines
• Full-stack engineering skills, including modern front-end frameworks (e.g., Next.js/React), back-end services, RESTful APIs, microservices, and cloud-native deployment (e.g., containers, Kubernetes)
• Deep expertise in LLMs and generative AI, including transformer-based model architectures and their practical application
• 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/No
  
  SQL databases
• Expertise designing AI systems in cloud-native, distributed environments across AWS, Azure, and GCP
• Proficiency with infrastructure as code, including Terraform, for provisioning and managing cloud environments
• 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
• Hands-on experience with LLM application stacks, including orchestration frameworks (e.g., Lang…