Sr Product Architect(ISG)

Job Summary

Sr Product Architect ISG will define and optimize advanced Generative AI and Machine Learning driven product architectures and validation strategies for life sciences research and development solutions ensuring robust verification and validation outcomes in a hybrid work model. The role focuses on scalable design quality excellence and measurable value for customers and society.

Responsibilities

• Architect end to end product solutions that integrate Generative AI and Machine Learning capabilities into life sciences research and development platforms to deliver reliable data driven insights for global customers.
• Define scalable technical architectures that align with ISG product strategy by harmonizing analytics pipelines validation frameworks and deployment approaches for complex scientific workflows.
• Lead design of model lifecycle processes including data ingestion feature engineering training evaluation and monitoring to ensure consistent performance and traceability of advanced AI solutions.
• Drive creation of robust validation test execution strategies that cover functional performance usability and compliance aspects for AI enhanced products in regulated life sciences environments.
• Collaborate closely with cross functional teams including data science software engineering product management and quality to translate scientific requirements into clear system and component architecture specifications.
• Provide guidance on verification and validation methods tailored to Machine Learning models including test design scenario coverage model robustness assessment and continuous quality monitoring.
• Optimize architecture for hybrid work delivery by defining modular components standardized interfaces and remote friendly collaboration practices that support distributed teams and efficient iteration.
• Evaluate and select appropriate AI and data technologies by assessing scalability interoperability security and long term maintainability to uphold enterprise standards and customer expectations.
• Develop and review detailed architectural documentation including diagrams data flows and validation plans to ensure shared understanding and traceable decision making across the organization.
• Mentor senior technical contributors in applying best practices for Generative AI solution design model validation and verification workflows to raise overall engineering maturity.
• Partner with stakeholders to identify opportunities where AI driven automation and intelligent analytics can improve research outcomes reduce manual effort and accelerate innovation for scientific communities.
• Ensure that validation test execution and verification activities align with regulatory expectations and internal quality frameworks for life sciences solutions to protect patient safety and data integrity.
• Champion architecture decisions that enhance societal impact by enabling more accurate experiments faster discovery cycles and trustworthy AI usage in scientific products.
  
  

Qualifications

• Demonstrate extensive experience in designing and delivering complex product architectures that incorporate Generative AI and Machine Learning capabilities for enterprise scale solutions.
• Possess strong hands on background in validation test execution including planning test case design coverage analysis and defect management for software and AI components.
• Show deep familiarity with life sciences research and development verification and validation practices including risk based testing traceability and documentation of scientific systems.
• Apply advanced knowledge of data engineering model evaluation techniques and AI performance optimization to ensure stable and efficient operation of analytics workloads.
• Bring proven expertise in collaborating with multidisciplinary teams such as data scientists engineers domain experts and quality professionals to deliver integrated solutions.
• Utilize strong communication and documentation skills to explain complex architectural decisions AI constraints and validation strategies in clear and concise language.
• Exhibit experience working in hybrid environments by using modern collaboration tools and processes that support effective design reviews and distributed implementation.