• Serves as the visionary leader in Bioinformatics AI application development in a clinical genetic testing setting. • Provides technical guidance and hands-on support towards building company’s next-generation bioinformatics AI platform. • Identifies, prototypes, and develops state-of-the-art AI applications to revolutionize clinical testing and genomic analysis workflow. • Designs, develops, evaluates, and deploys novel AI solutions to gain valuable data insights based on the genetical, phenotypical, and clinical datasets. • Evaluates, adopts, and customizes GenAI models based on both internal and external datasets to build next-generation clinical genetic testing platforms. • Supports both internal and external data requirements by leveraging AI and GenAI capabilities to keep up with the increasing demands of the business. • Collaborates in a multidisciplinary and regulated clinical diagnostics environment with geneticists, bioinformaticians, software engineers, and IT infrastructure professionals.

• Conduct a research study with Legal professionals • Collect real-world professional scenarios to help train AI models • Submit scenarios resolved through conversation or consultation • Follow validation criteria for scenario submissions

• Research and design Agent execution framework, providing standardized runtime environment for intelligent agents • Implement tool call orchestration mechanism, supporting unified abstraction for function calling, API integration, and external system interaction • Build execution sandbox environment to ensure safety and controllability of Agent operations • Design task decomposition and planning engine, supporting automatic breakdown of complex goals and execution path optimization • Implement execution state tracking and anomaly recovery mechanisms to ensure reliability of long-running tasks • Design hierarchical memory architecture, covering storage and retrieval mechanisms for working memory, short-term memory, and long-term memory • Research memory compression and summarization techniques, enabling efficient storage of massive interaction history while preserving key information • Build context-aware memory system, supporting multi-dimensional memory association based on time, task, and user • Develop memory retrieval augmentation mechanisms, achieving deep integration of RAG and Agent memory • Explore memory forgetting and update strategies, balancing memory capacity with information timeliness • Research multi-Agent system architecture, design communication protocols and collaboration mechanisms between Agents • Implement role specialization and task allocation algorithms, supporting orchestration of expert Agents, coordinator Agents, executor Agents, and other roles • Build consensus achievement and conflict resolution mechanisms to handle decision disagreements among multiple Agents • Design Agent social behavior norms, simulating communication, negotiation, and feedback patterns in human team collaboration • Explore emergent behavior and collective intelligence, researching self-organization and adaptive capabilities in multi-Agent systems • Design Agent evaluation and benchmarking system, establishing quantitative capability metrics • Build Agent behavior interpretability framework, supporting decision process tracing and attribution analysis • Research Agent safety alignment mechanisms to prevent risks such as unauthorized operations, harmful outputs, and goal drift • Track cutting-edge Agentic AI research and translate academic achievements into engineering practice.

• delivering solutions that help clients identify payment integrity issues, reduce healthcare costs, or improve quality of outcomes. • conducting independent model validation of existing models for benchmarking, assessment, and gauging effectiveness. • determining aspects of model drift and related data drift for the purpose of model risk management (MRM) to reduce risk and drive revenue growth. • applying deep expertise with AI/ML/GenAI model development, including hands-on with model building and evaluation. • driving improvements in model monitoring activities including methods for model registration, model metadata management, and conceptualizing related tools and techniques.