Head of Engineering

EnrollHere, Inc. is a fast-growing Medicare distribution SaaS platform serving agents, agencies, and carriers. The Head of Engineering will be responsible for leading the engineering organization, focusing on improving product delivery velocity, quality, and security while fostering a strong engineering culture.

Responsibilities:

• 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 possess
• 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
• Build 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
• Set expectations for how the team collaborates: thoughtful code reviews, healthy debate, and a shared commitment to raising the bar on quality and reliability
• 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
• 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
• Lead and develop a team of engineers across varying levels of seniority, experience, and working style that does not compromise the culture, but promotes it
• Build a collaborative, entrepreneurial engineering culture — one where ownership is shared, standards are high, and people do their best work without burning out
• Hire, develop, and when necessary, make hard calls on team composition or individual performance accountability
• Create clarity of role and accountability for senior engineers, including those operating in informal leadership positions
• 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
• Establish a quality culture reinforced by AI and automation, using intelligent test generation, continuous regression detection, and clear quality gates so reliability and coverage are enforced by default, not inspection
• 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
• Ensure regulatory and contractual compliance is engineered into the platform, using automated controls, continuous validation, and auditable enforcement for HIPAA‑adjacent data handling and carrier obligations
• 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