Principal Software Engineer (Python, AI)

This is a principal‑level role for someone who has already operated at Staff or Principal Engineer level in a sizeable engineering organization and can point to specific platform decisions that they own. You’ll set the technical direction for our AI‑enabled platform: architecting secure, scalable, serverless systems on AWS, defining the patterns and governance practices that multiple teams adopt, and guiding where AI genuinely adds value across the stack. Your leverage comes from the systems, architectures, and standards you put in place — not from individual output. You’ll mentor senior engineers, lead org‑wide design decisions, and be the person other technical leaders turn to when the hard calls need to be made. You’ll still write code, but that’s not where your impact is measured. If you’ve led complex platform or AI initiatives at scale, have strong opinions about AI governance in production systems, and want the scope to shape an entire platform’s future, this is that role.

Responsibilities

• Shape engineering strategy with broad organizational impact — you’ll influence long‑term architectural direction across multiple teams and products, not just within your own squad.
• Drive platform evolution by identifying cross‑cutting pain points and leading the design of secure, scalable, reusable solutions built on AWS and modern serverless and microservice patterns.
• Lead architectural discussions and design reviews where the stakes are real — making clear trade‑offs around performance, security, reliability, and maintainability, and getting alignment across teams with competing priorities.
• Own AI architectural decisions end‑to‑end: design AI‑enabled systems with built‑in governance, monitoring, and regulatory readiness baked in from the start, not retrofitted.
• Define where AI adds genuine value and where it doesn’t.
• Act as the organization’s AI thought leader — educate engineering teams on model behavior, agentic systems, and responsible AI practices, and raise the overall maturity of how we design and deploy AI.
• Mentor and stretch senior and staff engineers, building technical leadership depth across the organization and holding a high bar for engineering standards.

Essential Qualifications

• Principal‑level track record at scale. 10+ years of software engineering experience, including at least 3 years operating at Staff or Principal Engineer level in an organization of 100+ engineers.
• You’ve led large, complex platform or AI initiatives where you were the decision‑maker, not a contributor.
• Deep, opinionated AWS platform expertise. You’ve designed multi‑account AWS architectures and made the call on when serverless is the wrong choice.
• You work fluently with Infrastructure as Code — Terraform or CDK preferred — and have strong views on observability, resilience, and security that you’ve translated into org‑wide patterns.
• Production AI systems experience. You’ve shipped AI‑enabled systems — RAG pipelines, agentic frameworks, LLM orchestration, or similar — into production and can discuss the architectural decisions, failure modes, and trade‑offs involved.
• AI governance in practice. You’ve defined and implemented AI governance in real production environments — bias and privacy checks in pipelines, audit‑ready monitoring, AI usage policies — not just read about it.
• API design and backend platform delivery. Proven track record designing and shipping RESTful APIs and backend platform components using Node.js/TypeScript.
• Org‑level communication and influence. You can align stakeholders on complex technical decisions, run architecture forums, and mentor other senior technical leaders.

Nice to Have

• Experience building platform components or SDKs that multiple product teams depend on, including the documentation and enablement work that makes them adoptable.
• Involvement in architecture forums, tech councils, or similar bodies that define org‑wide standards — either running them or playing a leading role.
• Experience optimizing cost and performance for data and AI‑heavy workloads at scale, including capacity planning and infrastructure tuning.
• Leadership of major modernization efforts — legacy migration to serverless, multi‑region architecture, or platform consolidation across product lines.
• A public technical presence — conference talks, open‑source contributions, published writing — that demonstrates thought leadership beyond your current organization.