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

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Head of AI - AI-Native Healthcare SaaS based in India. This is a hands-on AI leadership role at the intersection of healthcare, SaaS, and advanced AI engineering. You will own the AI roadmap across products that support clinical insights, care operations, and AI infrastructure. The role combines technical architecture, production reliability, team leadership, and AI economics. You will build and lead a growing AI engineering function while remaining close enough to the technology to review systems and solve complex issues. Your work will focus on reliable, explainable, scalable AI workflows rather than experimental prototypes alone. You will collaborate closely with engineering and clinical stakeholders to turn emerging AI capabilities into trusted production solutions. This is an opportunity to shape an AI organization from the ground up within a fast-moving, clinically relevant environment. This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Head of AI - AI-Native Healthcare SaaS based in India. This is a hands-on AI leadership role at the intersection of healthcare, SaaS, and advanced AI engineering. You will own the AI roadmap across products that support clinical insights, care operations, and AI infrastructure. The role combines technical architecture, production reliability, team leadership, and AI economics. You will build and lead a growing AI engineering function while remaining close enough to the technology to review systems and solve complex issues. Your work will focus on reliable, explainable, scalable AI workflows rather than experimental prototypes alone. You will collaborate closely with engineering and clinical stakeholders to turn emerging AI capabilities into trusted production solutions. This is an opportunity to shape an AI organization from the ground up within a fast-moving, clinically relevant environment. Accountabilities AI Strategy & Architecture: Define and execute the AI roadmap across assessment, care/practice, and infrastructure products, making key decisions around model selection, orchestration frameworks, architecture, and build-versus-buy approaches. AI Engineering Leadership: Build and lead an AI engineering team, initially managing one direct report and scaling toward a team of approximately 3–4 engineers. Own hiring, performance expectations, coaching, mentoring, and team culture. Production AI Infrastructure: Design, implement, and evolve scalable production pipelines supporting LLM orchestration, clinical NLP, AI-generated insights, and other AI workflows, with strong emphasis on reliability and graceful failure. Monitoring & Incident Management: Establish monitoring, testing, alerting, logging, and incident-response practices that enable rapid detection, investigation, and resolution of AI pipeline failures. AI Cost Management: Treat inference and AI usage costs as core production constraints by tracking per-workflow and per-customer economics, optimizing model selection, token consumption, and overall infrastructure efficiency. Traceability & Explainability: Build observability systems that make AI behavior understandable and debuggable, allowing teams to trace inputs, decisions, outputs, and failures throughout complex workflows. Documentation & Governance: Create AI engineering playbooks, testing standards, runbooks, decision records, and governance processes that make technical knowledge transferable and support clinical compliance. Clinical AI Innovation: Evaluate emerging models, frameworks, orchestration tools, and agentic approaches, identifying opportunities to improve clinical outcomes while balancing accuracy, latency, reliability, safety, and cost. Cross-Functional Leadership: Partner with engineering, product, clinical, and executive stakeholders to align AI architecture and delivery with business priorities, product strategy, and clinical needs. Organizational Development: Establish clear ownership and accountability across the AI function while developing an engineering culture centered on reliable execution, continuous improvement, and measurable outcomes. Requirements Professional Experience: 8–14 years of software engineering experience, including at least 3 years leading AI/ML teams in an industry environment, with a proven record of delivering AI products used by real customers. Production AI: Extensive experience with LLM orchestration frameworks such as Dify, LangChain, LlamaIndex, or equivalent, including designing and managing production-scale AI pipelines. Healthcare & Regulated Environments: Hands-on experience deploying AI in healthcare or another regulated industry, with a practical understanding of compliance and governance requirements. Architecture: Strong systems and infrastructure mindset, with the ability to evaluate scalability, reliability, observability, maintainability, and cost efficiency beyond prompt-level solutions. Modern AI Workflows: Experience with agentic AI workflows and AI-assisted coding pipelines, and an understanding of how these approaches can be incorporated into modern engineering organizations. People Leadership: Experience managing teams of 2–5+ AI engineers, including hiring, performance management, mentoring, and professional development. Communication: Excellent English communication skills, particularly in asynchronous environments, with the ability to produce clear technical designs, decision memos, risk assessments, and documentation. Startup Experience: Proven ability to operate effectively in startups or high-growth environments characterized by ambiguity, limited resources, rapid change, and tight delivery timelines. Healthcare SaaS: Experience with healthcare SaaS, EHR, billing, clinical workflows, or HIPAA-related environments is strongly preferred. Clinical AI: Knowledge of clinical NLP, behavioral health applications, or related healthcare AI use cases is highly valued. AI Infrastructure: Experience building AI infrastructure platforms rather than only individual AI applications, along with familiarity with observability and monitoring strategies. AI Economics: Experience managing AI cost budgets and optimizing inference and model usage at scale. Additional Knowledge: Familiarity with FHIR/HL7, multi-tenant SaaS AI deployments, SOC 2 or comparable compliance frameworks, mental or behavioral health, and FDA AI/ML regulations is a plus. Research & Open Source: Contributions to ML/NLP research or open-source projects are welcome, although practical industry delivery experience is prioritized. Leadership Mindset: Strong ownership, analytical thinking, practical problem-solving, and the ability to balance innovation with reliability, clinical accuracy, safety, and business impact. Benefits Compensation positioned well above the typical market range for senior AI leadership roles within Indian startups. Fully remote work available across India. Equipment allowance provided. India local holidays recognized and observed. Flexible paid time off policy. Funding available for leadership development and professional coaching. Direct and frequent communication with the CEO, without unnecessary management layers. Opportunity to build and shape an AI engineering organization from the ground up. Opportunity to work on production AI systems with meaningful applications in healthcare and clinical care. Flexible working arrangements, with evening IST hours and approximately 4–8 hours of daily overlap with US Pacific Time; schedules can be proposed around team collaboration needs. Leadership availability expected during critical deployments or production incidents. Inclusive, ownership-driven environment focused on building reliable, high-impact AI products. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1