Senior Full Stack (AI) Engineer

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, GitHub, AWS, Azure, and GCP), and ownership of enterprise architecture, governance, and innovation. This is a full-time position under Nexus for Pyramid Systems. Responsibilities 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 GitHub-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 1000-53 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 DevSecOps pipelines and CI/CD automation (e.g., GitHub 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 DevSecOps 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) Knowledge/Skills/Abilities: Expert-level proficiency across the platform stack (Next.js, Terraform, GitHub, 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/NoSQL 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 stac