Head of Engineering – Agentic AI Healthcare SaaS

Role Overview

• You will oversee delivery for all products — scope management, release cadence, quality controls, and stakeholder alignment.
• You will own the design and execution of our agentic software development lifecycle:
• Define how AI agents participate in coding, testing, code review, and documentation — and where human engineers must intervene.
• Build quality gates that catch AI sloppiness. Every AI-generated artifact needs a human verification step calibrated to the risk level.
• Determine which agents we use, how they're configured, what guardrails they operate within, and how engineers supervise their output.
• Define approved tools, IP protection policies, and ensure AI accelerates development without introducing risk.
• Measure what's working, what's failing, and iterate. The playbook doesn't exist yet — you'll write it.
• You will directly manage the engineering team — hiring, performance, coaching, feedback, conflict resolution, and retention.
• You own the architecture across the full stack: web applications, APIs, infrastructure, and AI integrations.
• You will build the release pipeline — CI/CD, environments, quality checkpoints, deployment automation.
• You own engineering security: access controls, secrets management, audit trails, and SDLC security.
• You will build the engineering team — define roles, maintain hiring standards, run technical interviews, and make hiring calls.

Requirements

• First-principles thinker. You reason from fundamentals, not pattern-match from past jobs. When faced with a problem nobody has solved before — like designing quality gates for agent-generated clinical software — you figure it out.
• High learning velocity. The agentic SDLC is new territory. You may not have done this exact thing before, but you learn fast enough that it doesn't matter. You've repeatedly moved into unfamiliar domains and become effective quickly.
• Ownership-oriented. You view engineering leadership as outcome ownership, not task management. You step into chaotic delivery environments and create order — through clarity and accountability, not excessive process.
• Startup-proven. Your experience includes startups, not exclusively large enterprises. You've shipped real products to real users under real deadlines. You know the difference between building something and delivering it.
• Technically credible. You can write, review code, debug production issues, understand systems architecture at scale
• Direct and fair. You give feedback that develops engineers. You handle conflict promptly. Your teams trust you because you're honest, consistent, and keep them focused.
• Raw intellect is non-negotiable. This role demands someone who can operate in uncharted territory — designing human-agent engineering workflows, making architectural calls with clinical data constraints, and building an engineering org model that doesn't have an established playbook. We weight intellectual horsepower heavily.
• Strong academic foundations from a rigorous technical program (IIT, NIT, BITS, or demonstrably equivalent). We value the problem-solving discipline these programs develop.
• Experience building and leading engineering teams (5-15 people) at startups or high-growth companies. You've shipped SaaS products, not just maintained them.
• Familiarity with or strong interest in agentic AI workflows — using AI agents in the development process, not just as autocomplete. If you've already experimented with agent-driven development, that's a significant plus.
• Healthcare/healthtech experience is strongly preferable — especially around compliance, PHI handling, or clinical workflows. Not required, but it accelerates your ramp.
• You've thought seriously about AI governance in engineering — IP, security, quality, audit — and have opinions, not just questions.

Tech Stack

• SDLC