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.
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)
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
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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 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)
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 stacks, including orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel), embeddings, vector databases, and prompt engineering
Executive communication skills with experience influencing senior leaders
Demonstrated ability to own solutions end to end, from discovery and prototyping through production deployment, integration, and ongoing support
Ability to balance strategic vision with deep hands-on technical execution
U.S. Citizenship required
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
12–15+ years of software engineering experience, including significant leadership responsibility
8+ years of applied AI/ML experience, including building and deploying production systems (LLMs, generative AI, and large-scale or distributed model systems) Expert-level full-stack development experience, including designing production-grade AI systems, data pipelines, and microservices-based architectures
Deep experience with cloud platforms (Azure, AWS, GCP), including FedRAMP environments
Hands-on experience building full-stack applications with Next.js and managing infrastructure as code with Terraform
Experience with AI platforms and architectures (e.g., AWS Bedrock, Azure OpenAI Service, Google Vertex AI, RAG, agents)
Proven success delivering enterprise-scale systems and modernization programs
Strong background in microservices, APIs, distributed systems, and DevSecOps practices
Demonstrated ability to translate AI research into production systems
Active clearance (Public Trust, Secret, or higher) preferred
Startup or early-stage company experience preferred
Experience using AI coding tools (e.g., Claude Code, OpenAI Codex) to accelerate development preferred
About Pyramid Systems Inc
IT Services & Consulting200 employeesFounded 1995
Employee-owned federal IT consulting firm modernizing mission-critical systems for U.S. government agencies.
