1. Purpose:

Lead the strategic development and operationalization of HAYAVerse, HAYA Therapeutics’ AI-native Therapeutic Intelligence Organization and enterprise intelligence operating system. This role is not focused on deploying isolated AI tools, but on redesigning how a biotechnology company operates through persistent enterprise intelligence, multi-agent orchestration, and AI-native workflows spanning R&D, clinical, business, regulatory, operations, finance and corporate strategy.

Accountabilities

• Architect and operationalize HAYAVerse as HAYA’s unified enterprise intelligence platform integrating scientific, clinical, operational, and strategic workflows.
• Drive the enterprise-wide transformation strategy transitioning HAYA from legacy siloed workflows toward an AI-native operating model.
• Architect our AI native systems that increase decision velocity, decision quality, and cross-functional intelligence orchestration across the company. Design and deploy multi-agent systems, enterprise retrieval frameworks, and operator digital twins supporting scalable human-AI collaboration.
• Transform institutional knowledge, scientific interactions, and operational workflows into reusable enterprise intelligence assets.
• Coordinate internal "speed-validation" cohorts and human-in-the-loop testing with Subject Matter Experts (SMEs) to secure robust validation of the AI tools' practical utility.
• Evaluate phased platform deployment to ensure AI systems successfully eliminate operational friction and accelerate scientific, clinical, and regulatory workflows.
• Act as the enterprise translator between AI engineering teams and scientific, operational, and regulatory stakeholders to ensure solutions satisfy real-world biotech execution requirements. Direct a fragmented team of 10+ specialized external contractor roles across four project phases to ensure the timely and precise construction of the HAYAVerse platform.
• Control multi-phase AI infrastructure and platform budgets while governing compute utilization, operational scalability, enterprise security and long-term infrastructure efficiency.

3. Required Knowledge and Experience

• Experience leading enterprise AI transformation initiatives or AI-native operational systems preferred.
• Expertise in bridging AI engineering, enterprise systems, scientific workflows, and operational strategy within highly complex organizations. Proven capability in managing and aligning fragmented, highly specialized external vendor teams across complex, multi-phase technology projects.
• Deep understanding of LLM infrastructure and compute economics necessary to manage substantial CapEx budgets and strictly govern monthly OpEx quotas.
• Advanced communication and liaison skills required to extract requirements and translate complex technical concepts for scientific and regulatory stakeholders.
• Demonstrated knowledge of driving organizational change management and coordinating user validation processes (such as human-in-the-loop testing) within a scientific or technical environment.
• Experience in biotech, life sciences, healthcare, or other regulated technical environments strongly preferred.
• Strong understanding of:
• Multi-agent systems
• LLM orchestration
• Retrieval-augmented generation (RAG)
• Enterprise knowledge systems
• AI infrastructure and compute economics

4. Leadership Profile

The successful candidate will demonstrate:

• Enterprise systems thinking
• Organizational transformation leadership
• Strong execution discipline
• Ability to translate complexity across technical and non-technical stakeholders
• Capability to align fragmented teams around a unified strategic vision