Head of Engineering

About the role

This is a foundational leadership role. You will own responsibility for the engineering organization — its people, architecture, delivery, and culture — at a moment when the company has the resources to build something durable and the urgency to build it fast. You are not solely a manager; you are an engineering leader. You work with senior engineers to solve complex issues, establish processes that scale, perform thorough code reviews, and make impactful architectural decisions. Additionally, you demonstrate discernment in delegating responsibilities, providing constructive challenges, and knowing when it is appropriate to step back.

The mandate is clear: increase the velocity, quality, and security of product delivery — and do it by building a team and engineering culture that does not depend on heroics. Artificial Intelligence, automation, and strategic partnerships are primary levers, not afterthoughts. The buck stops with you.

Requirements

• What You Will Own:
  • Engineering Execution: Own end-to-end engineering delivery across the full product surface — enrollment workflows, the commission engine, AI-driven compliance scoring, telephony infrastructure, and the underlying platform that ties it all together.
  • Drive sprint execution, release cadence, and delivery predictability without losing the pace a PE-backed growth company possesses.
  • Establish engineering processes that scale without the bureaucracy that kills momentum — lightweight rituals, clear ownership, and fast decision-making.
  • Transform the Product Development Lifecycle to incorporate agents and autonomous capabilities driving increased innovation.
  • Create a culture where engineers feel real ownership of outcomes, not just tickets. That means clarity of priorities, transparency around tradeoffs, and a bias (not hard line) toward finishing work, not starting more of it.
  • Create an environment where engineering, QA, and product operate as one delivery unit — aligned on goals, honest about risks, and accountable to the same definition of "done."
  • Anchor decisions in customer impact. Engineers should understand how their work shows up in an agent's workflow, a carrier's integration, or a beneficiary's enrollment experience — and use that context to guide tradeoffs, simplify complexity, and eliminate friction.
  • Model the behaviors you expect, such as: direct communication, curiosity, urgency without panic, and a willingness to roll up your sleeves when the team needs it.
• Architecture and Technical Direction:
  • Own technical direction and architectural strategy across the platform.
  • Make pragmatic architectural decisions — right-sized to where the company is today, with a clear-eyed view of where it needs to go.
  • Manage technical debt actively and transparently — it does not need to be zero, it needs to be known, tracked, and directionally improving.
  • Identify and implement AI and automation capabilities that materially improve engineering velocity, code quality, and security — not as a pilot program but as a core operating practice.
  • Evaluate and leverage technology partnerships that accelerate capability without creating dangerous dependencies.
  • Build a team that treats AI tooling as a standard part of the engineering workflow.
  • Partner across teams to build an AI innovation roadmap that puts customers first, woven into the product naturally — not bolted on.
• Platform Reliability and Security:
  • Own the organization's security posture as an AI-augmented engineering practice, embedding automated code analysis, threat detection and policy enforcement directly into the development and deployment lifecycle.
  • Improve platform resilience through AI-driven observability and disciplined releases, leveraging anomaly detection and automated root-cause analysis to reduce incidents and eliminate repeat failures.
  • Translate compliance requirements into executable, AI-testable engineering standards in partnership with compliance and operations, ensuring consistency and repeatability across teams and releases.
  • Champion a data- and AI-powered feedback loop across engineering, QA, and production, enabling earlier defect detection, automatic regression prevention, and continuous system-level learning.

Qualifications

• 8-10 years of progressive engineering experience, with at least 3-5 years in a senior engineering leadership role owning a team and a product.
• Demonstrated ability to expand engineering capacity through a combination of AI-native development practices and effective use of off/near-shore or distributed teams, increasing output and leverage without sacrificing quality, velocity, or accountability.
• Hands-on engineering capability — you have written production code and can still do it when it matters.
• Experience leading engineering in a SaaS product company; regulated industry experience (healthtech, insurtech, fintech) strongly preferred.
• Proven track record of improving engineering velocity, quality, and security with specific, measurable outcomes.
• Experience with cloud-native platforms (GCP preferred), containerized workloads (GKE/Kubernetes), and modern backend architectures.
• Familiarity with AI/ML integration in production SaaS environments.