Head of Engineering - Agentic AI Healthcare SaaS | Zenara Health

Pay close attention here. If you are an engineering manager who primarily conducts standups and writes status reports, this role may not suit you.

We are transitioning from the product stage to the commercial stage — multiple products, real customers, sensitive clinical data. Our engineers are already delivering, and now we need cohesive engineering leadership to transform exceptional individual contributions into collective organizational success.

You will oversee delivery for all products — scope management, release cadence, quality controls, and stakeholder alignment. If something is delayed, it's your responsibility. If a product ships smoothly, you can claim that success. You'll shield engineers from scope changes and give the CEO predictable delivery rather than last-minute heroics.

This is the core of what makes this role unique. You will own the design and execution of our agentic software development lifecycle:

• Human-agent workflow design: Define how AI agents participate in coding, testing, code review, and documentation — and where human engineers must intervene.
• Maker-checker patterns: Build quality gates that catch AI sloppiness. Every AI-generated artifact needs a human verification step calibrated to the risk level — a UI tweak needs a different checkpoint than a database migration.
• Agent orchestration: Determine which agents we use, how they're configured, what guardrails they operate within, and how engineers supervise their output.
• AI tool governance: Define approved tools, IP protection policies, and ensure AI accelerates development without introducing risk — especially given the sensitivity of clinical/PHI data.
• Continuous refinement: This model is new. You'll measure what's working, what's failing, and iterate. The playbook doesn't exist yet — you'll write it.

If you don't have a strong, opinionated perspective on how AI agents should participate in the engineering process — beyond "we use Copilot" — this role is not for you.

You will directly manage the engineering team — hiring, performance, coaching, feedback, conflict resolution, and retention. The team is small and high-leverage; every person matters disproportionately. You'll set the culture and performance bar. Difficult conversations happen early. Engineers will want to work with you because you are fair, direct, and invested in their growth.

You own the architecture across the full stack: web applications, APIs, infrastructure, and AI integrations. You'll make trade-off calls — speed vs. rigor, refactor vs. ship, infrastructure vs. features. You should be capable of reviewing code, debugging production issues, and challenging architectural decisions with substance. In a clinical data environment, architectural choices carry compliance and safety implications — you'll factor those in.

You will build the release pipeline — CI/CD, environments, quality checkpoints, deployment automation. Chaotic releases end. You'll create a system that lets the team (and their agents) ship confidently and on a predictable cadence.

You own engineering security: access controls, secrets management, audit trails, and SDLC security. Healthcare data — especially mental health data — demands this. You'll also ensure AI-generated code and agent workflows meet audit and compliance requirements. Enforce rigor without bureaucracy.

You will build the engineering team — define roles, maintain hiring standards, run technical interviews, and make hiring calls. You're building the organization that takes the company from startup to scale. Given our agentic model, you'll also need to think differently about team composition: fewer engineers, higher caliber, optimized for agent supervision rather than raw code output.

• Engineering consistently delivers — the CEO no longer chases delivery updates.
• The agentic SDLC is operational: agents produce, humans verify, AND quality holds. The maker-checker model is documented, measured, and improving.
• Release cadence is predictable and accelerating; quality standards are enforced.
• Engineers have clear ownership, regular coaching, and unambiguous expectations.
• Architectural decisions are documented, reasoned, and account for clinical data sensitivity.
• Security and compliance posture is strong — including for AI-generated code and agent workflows.
• The hiring pipeline is active — you're building a team optimized for the human-agent model.
• AI tools and agents operate within a clear governance framework — speed without risk.
• The engineering organization is healthier, faster, and more reliable than when you arrived.

• 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.