Head of Engineering

***This is not a role with 2070 Health*

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Role Title: Head of EngineeringLocation: Remote across India (~6 hours overlap with US Pacific time)Type: Full-timeReports to: Founder-CEOCompensation: Well above market for Indian startups at this level. We pay for the caliber we're hiring.

About the Company

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.

About the Role

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.

What You Will Own (Everything)

1. Delivery Outcomes

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.

2. Agentic SDLC & AI Governance (The Differentiator)

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.

3. Engineering Team

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.

4. System Architecture

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.

5. CI/CD and Release Engineering

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.

6. Security & Compliance Posture

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.

7. Hiring & Team Building

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.

Your First 90 Days

Week 1-2: Immerse yourself. Meet each engineer individually. Understand every product, deployment, and pain point. Map the current human-agent workflows — what's working, what's brittle. Identify delivery risks and the single biggest bottleneck. Build trust through listening, not announcements.

Month 1: Establish a regular delivery cadence. Define the release process and quality standards. Create communication rhythms (standups, retros, planning). Audit the current agentic workflows — identify where AI output lacks sufficient human review. Begin surfacing risks early and reliably, relieving the CEO from delivery oversight.

Month 2-3: Standardize CI/CD across all products. Implement maker-checker quality gates for AI-generated code. Design the AI governance framework — approved tools, IP protection, PHI safeguards for agent workflows. Initiate architecture assessment with a clear roadmap (not a rewrite). Begin hiring to fill gaps. Build the engineering runbook. Establish feedback and coaching routines.

Ongoing: Own engineering completely. Ship reliably. Refine the agentic SDLC continuously. Grow the team. Raise the performance bar. Make the CEO confident that engineering is in expert hands.

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. 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.

Who You Are

• 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.

What We Look For (Candidate Profile)

• 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.