Principal Web Engineer (Architecture & Platform-(SaaS)

The Principal Web Engineer (Architecture & Platform) is a senior individual contributor responsible for the architectural direction, technical integrity, and long-term scalability of Stanbridge University’s core web platform and supporting services, built primarily with Laravel (PHP 8.x) and Vue.js (Vue 3).

This role provides architectural ownership, establishes and reinforces engineering standards, and accelerates high-quality delivery across the platform. This role operates within a modern SaaS, agile development environment A key focus of this position is to materially increase delivery speed and developer productivity through the thoughtful adoption of modern, AI-assisted engineering practices—including AI-enabled coding, review workflows, testing, debugging, and documentation automation—while maintaining rigorous standards for security, correctness, accessibility, and maintainability.

This is not a people-management role. Impact is measured through shipped outcomes, improved throughput and cycle time, reduced rework, and sustained platform quality.

Remote applicants are encouraged to apply. Candidates located within or near Stanbridge University’s campus footprint, and able to participate in occasional on-campus meetings and collaboration, are strongly valued.

Essential Duties and Responsibilities:

Architecture & Technical Strategy

• Own the architectural direction of the university’s core web platform, ensuring intentional evolution over time.
• Lead architectural decision-making for high-impact initiatives; document decisions using Architectural Decision Records (ADRs), diagrams, and clear implementation guidance.
• Define and evolve platform strategy across modularity, API contracts, data access patterns, background processing, queues, caching, and state management.
• Identify architectural risks related to scalability, performance, maintainability, and security; propose pragmatic, executable solutions with clear tradeoffs.
• Establish and reinforce consistent engineering patterns to prevent technical drift and unnecessary complexity.

Hands-On Engineering & Implementation

• Design, build, and maintain production features and platform capabilities using Laravel (PHP 8.x) and Vue 3 (Composition API), with TypeScript where appropriate.
• Own complex, cross-cutting, or high-risk technical implementations; unblock delivery when architectural complexity becomes a bottleneck.
• Build and maintain reusable UI components using semantic HTML and responsive CSS/Sass; uphold WCAG 2.2 AA accessibility standards.
• Integrate with internal and third-party enterprise systems via REST/JSON APIs with robust error handling and resilience.
• Configure and optimize modern build tooling (e.g., Vite, Node.js, npm/pnpm) for performance, maintainability, and developer experience.

Code Quality, Review Standards & Engineering Enablement

• Serve as a high-signal reviewer for complex and architectural pull requests.
• Define and evolve coding standards, pull request expectations, conventions, and quality gates (testing, linting, review requirements).
• Identify recurring quality issues and address root causes through refactoring strategies, standards, and enablement.
• Mentor and elevate other engineers through technical guidance, pairing, and targeted feedback (without formal people-management responsibilities).

AI Tooling & Productivity Acceleration

• Drive adoption of high-leverage AI-assisted development practices to measurably improve delivery speed while preserving quality and operational safety.
• Evaluate, pilot, and standardize AI tooling and workflows across implementation, code review, testing, debugging, and documentation.
• Establish repeatable AI-first engineering playbooks, including prompt patterns, repository context practices, PR checklists, and enhanced definitions of done.
• Partner with QA and DevOps/SRE to integrate AI-enabled practices into CI/CD pipelines and the broader SDLC.
• Demonstrate measurable productivity gains (e.g., faster PR cycle times, reduced rework, fewer QA kickbacks) while maintaining security and maintainability standards.

Security, Safety & Responsible Use of AI

• Establish guardrails ensuring AI-assisted output remains secure, correct, and aligned with architectural standards.
• Ensure responsible use of AI tools in accordance with university policies and data-handling requirements.
• Apply secure-by-default practices aligned with OWASP, including input validation, authorization, secrets management, and dependency risk mitigation.
• Uphold responsible data practices consistent with FERPA and university policy, includin