About the role
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 foundations. Production & Platform Experience Mastery of software engineering fundamentals — architecture patterns, CI/CD, testing strategy, observability, and code quality — and a demonstrated record of establishing these standards at the organizational level. Expert-level understanding of security, privacy, and compliance
