Staff Engineer, GTM AI

Responsibilities

• Architectural Ownership: Serve as the technical authority for the AI platform, owning architectural direction and long-term system health across multiple business functions.
• Platform Evolution: Define and evolve the architecture of our data and AI platform — including data processing pipelines, integration layers, and the services that power real-time account intelligence.
• AI Governance: Define standards for AI service governance, evaluation, observability, performance, and cost management. Ensure AI capabilities are production-grade, auditable, and trustworthy.
• Technical Leadership: Act as a hands-on technical leader — contributing high-leverage code, mentoring, and raising execution standards across the organization. Guide engineers so that rapid prototyping is built on a sound, scalable foundation.
• Data Integrity: Own the "Golden Record" strategy — the system of record for account intelligence across the full revenue lifecycle. Establish durable data modeling standards to ensure consistency and long-term maintainability.
• Risk Anticipation: Anticipate structural and scaling risks, and guide systems toward the desired future state through deliberate, iterative modernization — before problems become visible to users.

Requirements

• 6–8+ years of software engineering experience, with a proven track record of owning complex system architectures.
• Deep expertise building secure, scalable enterprise platforms, with proven experience designing strictly idempotent APIs for high-throughput distributed systems.
• Strong focus on system reliability, observability, and engineering excellence.
• Ability to communicate complex architectural decisions clearly to both technical teams and executive leadership.
• Experience leading or mentoring engineering teams through rapid scaling phases.
• Fluency in Python and modern cloud infrastructure (AWS, PostgreSQL, event-driven architectures).
• Strong systems thinking and the ability to balance speed with maintainability.

Nice to Have

• Experience building AI/ML platforms or systems that serve real-time insights to business users.
• Background in revenue technology, CRM platforms, or enterprise GTM systems.
• Track record of building systems that maintain trust and accuracy as they scale — not just systems that handle load.
• Experience implementing data governance, validation, and monitoring frameworks across business-critical systems.
• Familiarity with AI evaluation frameworks and quality assurance for LLM-powered outputs.