Principal AI Software Engineer

Key Responsibilities

Technical Leadership & Organizational Enablement


* Define and drive the engineering vision across multiple teams, aligning technology direction with company-wide business objectives.


* Mentor and develop Engineers through structured coaching, architectural sponsorship, and deliberate investment in their growth as technical decision makers.


* Shape the engineering culture by establishing shared standards, raising the technical bar in hiring, and influencing how engineering competency is grown across Engineering levels.


* Partner with VPs of Engineering, Product, and other executives to co-create technical strategy, bringing a long horizon, systems-level perspective to roadmap and investment decisions.

Architecture, Implementation & Quality


* Define and steward reference architectures, frameworks, and engineering patterns that are adopted org-wide, creating leverage across teams rather than within a single one.


* Lead multi-quarter, high-ambiguity technical initiatives from problem definition through to sustained production impact, operating effectively without a defined playbook.


* Identify and resolve systemic, cross-cutting technical issues that span multiple teams or domains — distinguishing the root cause from the symptom and building durable solutions.


* Establish org-wide standards for AI system quality: testing strategies, evaluation frameworks, safety and reliability patterns, and deployment criteria for LLM-based systems.


* Publish internal frameworks, design patterns, and post-mortems that elevate engineering practice across the organization; contribute externally through writing, speaking, or open-source where appropriate.


* Champion engineering excellence as an organizational force to drive continuous improvement in practices, tooling, and developer experience at scale.

Required Skills & Qualifications

Technical Expertise


* 6+ years of experience with Python in production environments, with a track record of building systems that have scaled across organizations.


* 3+ years of experience designing, deploying, and operating language model–based solutions at production scale, including demonstrated ownership of LLM system reliability, evaluation, and iteration strategy.


* Deep, hands-on fluency with AI coding assistants (GitHub Copilot, Cursor, Claude Code) as a core part of engineering workflow, and a demonstrated ability to shape team-wide adoption and best practices around these tools.


* Recognized expertise in the AI/ML tooling ecosystem — including agentic frameworks, MCP, A2A protocols, and the evolving GenAI infrastructure landscape with a history of translating emerging technology into production grade capabilities.


* Proven ability to build systems that are simultaneously innovative, reliable, maintainable, and aligned with long-term business needs — with the judgment to know when to move fast and when to invest in fo...