Head of AI - AI-Native Healthcare SaaS | Zenara Health

About the Company Zenara Health is a tech-driven mental healthcare organization dedicated to improving both the accessibility and quality of mental wellness services. We combine AI-powered platforms with professional clinical care to deliver tailored and effective mental health solutions, ensuring a smooth digital experience for patients and providers alike. As a startup, we operate independently rather than as a mere department. About the Role This position is not suitable for an "AI strategy consultant" who specializes in creating presentations about potential future applications of AI. If your experience is mainly in research prototypes, offline benchmarks, or demo-driven AI, this opportunity may not be the right fit. Our focus is on developing systems that operate continuously, fail gracefully, and build trust over time. Our AI pipeline encompasses LLM orchestration, clinical NLP, assessment scoring, and active production workflows, and it is fully operational today. Your responsibility will be to expand it into a thoroughly documented, robust AI organization with established processes, team structure, and technical leadership. AI is the distinguishing factor at Zenara — it is not an add-on feature but the essence of our product functionality. You will be responsible for AI strategy across all Zenara products: our assessment tool (which provides AI-generated clinical insights), our care/practice platform (which features AI-assisted operations), and our AI infrastructure framework. You will collaborate closely with our Head of Engineering (your peer, not your subordinate) to integrate AI deeply into the platform. The industry is evolving rapidly. We need someone who can manage complex AI workflows, AI-assisted coding pipelines, and scale AI infrastructure. This role requires not just the deployment of models but also the development of organizational capabilities to deliver AI products at the pace of a startup. This role does not focus on "AI strategy" in terms of presentation-making. You will be actively involved: reviewing pipelines, troubleshooting orchestration failures, making model selection decisions, managing complex coding workflows, and delivering production AI solutions that improve clinical care. What You Will Own 1. AI/ML Strategy Across All Products You will establish the AI roadmap and architecture for our assessment and care/practice products, as well as our infrastructure platform. Your crucial decisions will include model selection, orchestration frameworks, and determining when to build or buy solutions. Your architectural decisions will shape the platform for years to come. 2. AI Engineering Team You will assemble and lead the AI engineering team, starting with one direct report and expanding to 3-4 team members. You will be responsible for hiring, setting performance standards, providing coaching, and cultivating the team's culture. Your goal will be to develop AI engineering talent capable of delivering at startup speed. 3. Production AI Infrastructure You will design and implement scalable AI pipeline infrastructure. This includes creating systems that facilitate LLM orchestration, clinical NLP, and AI-generated insights. You will also establish monitoring, testing, and incident response protocols for all AI workflows. If a pipeline fails, it will be your duty to rectify it. 4. AI Cost as Production Constraint You will regard AI costs as a key production constraint and ensure transparency in per-workflow and per-customer economics. Your role will involve monitoring and optimizing model usage, inference costs, and token consumption to ensure the economic viability at scale — not just in prototypes. 5. AI System Traceability and Explainability You will guarantee that AI system behavior is explainable and debuggable for internal teams, rather than merely for end users. You will create observability and logging mechanisms that allow for understanding AI decisions, tracing inputs through the pipeline, and methodically debugging any failures. 6. Documentation and Process You will develop an AI engineering playbook, documenting workflows, establishing testing standards, and creating runbooks for common incidents. Your effort will turn institutional knowledge from individual contributions into well-documented, transferable processes. 7. Clinical AI Innovation You will assess and incorporate new models, frameworks, and orchestration tools as the field continues to advance. Staying ahead of the curve, you’ll ensure that Zenara's AI capabilities remain at the forefront of clinical applications. Your First 90 Days Week 1-2: Immerse yourself fully. Gain a comprehensive understanding of all AI workflows currently in production, review the existing pipeline architecture, identify significant technical debt and key opportunities, and build rapport with the team through active listening. Month 1: Set documentation standards to begin capturing organizational knowledge. Define the AI testing and monitoring framework, create a technical roadmap that balances innovation with stability, and start monitoring AI costs by workflow. Month 2-3: Implement monitoring and alerting for production AI workflows, develop traceability and logging systems for AI decision debugging, initiate the hiring process for your first team member, and commence architectural improvements — not a complete rewrite but a clear evolutionary path. Establish an AI governance framework to ensure clinical compliance. Ongoing: Take charge of AI responsibilities. Introduce features that enhance clinical outcomes, expand the team, elevate performance expectations, and instill confidence in the CEO regarding the management of AI engineering. Values & Vibe (Who You Are) You see AI leadership as fundamentally about **ownership of outcomes** rather than merely exploring research. You thrive in chaotic AI environments, bringing order not through excessive academic rigor but through clarity, accountability, and effective execution. Your experience is rooted in startups or high-growth companies, rather than solely in research institutions. You find AI environments that resemble a black box—marked by heroic debugging rather than standardized practices—unacceptable. You possess a hands-on approach that allows you to review code, resolve issues, and assess architecture, while understanding your primary responsibility is to empower your team rather than to merely be a contributing member. You have a track record of successfully launching tangible AI products that serve real users within specified timeframes. You comprehend the distinction between an intriguing model and one that reliably supports clinicians. You have considered aspects related to AI safety, model governance, and production reliability, moving beyond a simplistic "we use the latest GPT" approach. You are aware of the trade-offs between model capabilities, costs, latency, and clinical accuracy, treating the economics of AI as a fundamental concern rather than an afterthought. What Success Looks Like AI systems consistently yield results, alleviating concerns for the CEO regarding pipeline failures The AI roadmap is clear and aligns with the overall product strategy AI costs are monitored on a per-workflow and per-customer basis , ensuring the business model scales effectively AI system behavior is debuggable , enabling the team to trace decisions and diagnose issues without extraordinary measures AI engineers have defined responsibilities, receive coaching, and benefit from continuous feedback Architectural decisions are well-documented and thoughtfully made Monitoring and alert systems detect issues before they are reported by users The hiring pipeline is active, as you are developing the team while managing current personnel Clinical teams have confidence in AI features due to their reliability The AI organization is more robust, efficient, and trustworthy than it was when you joined