Principal Engineer

Requirements

• 8+ years of software engineering experience, with substantial time spent in senior or principal-level hands-on roles
• Proven experience building and scaling SaaS platforms in production
• Strong full-stack engineering capability, with the ability to move between backend, frontend, and platform concerns as needed
• Strong architecture skills, especially in modular system design, service boundaries, shared patterns, and maintainability
• Experience operating in cloud-native environments and supporting production systems
• Experience with Python-based backend platforms, API-oriented architectures, and modern JavaScript frontend frameworks
• Strong code review judgment, with the ability to enforce standards while remaining pragmatic and delivery-oriented
• Experience with multi-tenant SaaS security and data isolation concerns
• Experience mentoring engineers and raising technical discipline on a small team
• Ability to critically review AI-assisted code contributions and establish team norms for responsible use of AI coding tools
• Ability to thrive in ambiguity and move fluidly between architecture, implementation, and technical problem-solving
• Excellent written and verbal communication skills, including the ability to explain tradeoffs clearly to both technical and non-technical stakeholders
• Hands-on experience building or integrating AI-powered software features or platforms in production, including LLM-enabled or workflow-driven application components
• Practical understanding of AI application architecture, including model integration patterns, retrieval workflows, prompt orchestration, evaluation, and guardrails
• Ability to represent the engineering team credibly in technical conversations involving AI use cases, implementation tradeoffs, and delivery considerations

Responsibilities

• Partner with product and engineering leadership to drive technical delivery through launch and early scale
• Contribute hands-on to the codebase across critical areas of the platform, especially on complex or high-risk work
• Contribute hands-on to AI-powered platform capabilities, including LLM-integrated workflows, retrieval patterns, prompt orchestration, and agent-oriented application components
• Review architecture-significant pull requests and ensure code aligns with established patterns, boundaries, and long-term design direction
• Help define and enforce engineering standards for code quality, testing, review discipline, observability, and release readiness
• Help define practical architecture and engineering standards for AI application quality, evaluation, guardrails, observability, and production readiness
• Reduce key-person dependency by documenting design decisions, spreading architectural context, and mentoring other engineers
• Reduce concentrated AI architecture knowledge risk by documenting design decisions, sharing context broadly across the team, and mentoring engineers on effective AI implementation patterns
• Improve the team’s use of AI-assisted coding and agentic development tools in a safe, disciplined, and repeatable way
• Help strengthen trust boundaries across authentication, authorization, tenant isolation, query execution, secrets handling, and cloud infrastructure
• Work closely with engineers across frontend, backend, AI orchestration, and infrastructure to unblock delivery and improve system cohesion
• Identify architectural hotspots, technical debt, and operational risks that could impair launch readiness or future scale
• Support the evolution of CI/CD, deployment confidence, and production-readiness practices
• Support technical solutioning discussions with clients and internal stakeholders for AI-driven use cases, translating implementation tradeoffs clearly and credibly
• Provide pragmatic technical leadership suited to a small, fast-moving company rather than a heavily layered enterprise environment

Skills