[Hiring] Head of Engineering - Agentic AI Healthcare SaaS @xponentiate

Role Description

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.

What makes this role different from every other Head of Engineering posting: Our engineering team is small by design. A handful of high-caliber engineers orchestrate AI agents that handle significant portions of the SDLC — from code generation to testing to documentation. Your job is not to manage 20 engineers writing code. Your job is to design, implement, and continuously refine the human-agent engineering model — including maker-checker workflows, quality gates for AI-generated output, and escalation protocols that ensure AI speed doesn't come at the cost of AI sloppiness.

This is a relatively new way of working. Very few people have deep experience running agentic SDLC at scale. We're not looking for someone who's done this exact job before — we're looking for someone with the raw intellectual horsepower to figure it out. High learning velocity, first-principles thinking, and comfort with ambiguity matter more than years on a resume.

You will report directly to the founder-CEO, take ownership of results, and shape the engineering organization. This is the technical co-leader seat — ultimately becoming the person the founder relies on to own all of engineering.

Qualifications

• First-principles thinker.
• High learning velocity.
• Ownership-oriented.
• Startup-proven.
• Technically credible.
• Direct and fair.

Requirements

• Raw intellect is non-negotiable.
• Strong academic foundations from a rigorous technical program (IIT, NIT, BITS, or demonstrably equivalent).
• Experience building and leading engineering teams (5-15 people) at startups or high-growth companies.
• Familiarity with or strong interest in agentic AI workflows.
• Healthcare/healthtech experience is strongly preferable.
• Thoughtful consideration of AI governance in engineering.

Benefits

• Compensation well above market for Indian startups at this level.

Company Description

Zenara Health builds GenAI-powered clinical decision support and workflow tools for mental health clinics. We integrate AI-driven platforms with professional clinical care to offer personalized and effective mental health solutions — from AI-enhanced evaluations to care coordination — creating a seamless digital experience for both patients and providers.

We are an AI-native organization. That's not a marketing label. Our engineering model is fundamentally built around AI agents participating in the software development lifecycle. We are a startup, not a department.

What You Will Own (Everything)

• Delivery Outcomes: Oversee delivery for all products — scope management, release cadence, quality controls, and stakeholder alignment.
• Agentic SDLC & AI Governance: Own the design and execution of our agentic software development lifecycle.
• Engineering Team: Directly manage the engineering team — hiring, performance, coaching, feedback, conflict resolution, and retention.
• System Architecture: Own the architecture across the full stack: web applications, APIs, infrastructure, and AI integrations.
• CI/CD and Release Engineering: Build the release pipeline — CI/CD, environments, quality checkpoints, deployment automation.
• Security & Compliance Posture: Own engineering security: access controls, secrets management, audit trails, and SDLC security.
• Hiring & Team Building: Build the engineering team — define roles, maintain hiring standards, run technical interviews, and make hiring calls.

Your First 90 Days

• Week 1-2: Immerse yourself. Meet each engineer individually. Understand every product, deployment, and pain point.
• Month 1: Establish a regular delivery cadence. Define the release process and quality standards.
• Month 2-3: Standardize CI/CD across all products. Implement maker-checker quality gates for AI-generated code.
• Ongoing: Own engineering completely. Ship reliably. Refine the agentic SDLC continuously.

What Success Looks Like

• Engineering consistently delivers — the CEO no longer chases delivery updates.
• The agentic SDLC is operational: agents produce, humans verify, AND quality holds.
• 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.