Principal AI Software Developer

Job Description

Job Description

Description:

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 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

Requirements

• 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