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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 inpu