• Ideate, develop and productionalize AI solutions for healthcare payer use cases • Design and implement state-of-the-art AI models and algorithms using Python and GCP Vertex AI • Collaborate with product teams to identify opportunities for applying AI techniques to solve business challenges and automate processes • Conduct research and stay up to date with the latest advancements in AI

• Designing and delivering enterprise-grade AI/ML and Data Science platforms within complex cloud ecosystems • Building serverless and containerised execution environments for ML and data workloads • Developing secure, scalable patterns for cloud Infrastructure-as-Code and deployments • Defining API standards, observability strategies and architectural guardrails • Establishing and evolving CI/CD pipelines with automation at the core • Leading and mentoring cross-functional engineering and data teams • Translating business requirements into pragmatic architectural solutions • Maintaining technical alignment with product, business and customer goals • Selecting technology approaches that meet non-functional requirements while optimising cost • Continuously researching emerging technologies and best practices and applying them where beneficial

• Play a critical role in advancing Cencora’s digital transformation agenda by building, deploying, and scaling AI and GenAI solutions • Work closely with AI Architects, AI Product Managers, and business stakeholders to translate initiatives into production-ready capabilities • Support the development and operation of Cencora’s internal GenAI platforms and AI-driven products; enabling data-driven decision making, intelligent automation, and digital workflows across the enterprise • Deliver new AI and GenAI capabilities that maximize business value, modernize core processes, and accelerate innovation in the global healthcare B2B market place • Emphasize cloud native engineering, responsible AI and scalable platform design

• Build and maintain data pipelines to support machine learning and analytics workflows. • Collect, clean, and transform large, complex datasets from engineering environments. • Develop and train predictive models for yield, performance, and anomaly detection. • Automate recurring data analysis tasks and integrate models into engineering processes. • Collaborate with design and software teams to embed AI-driven insights into products. • Create dashboards and visualization tools for reporting and decision-making. • Document code, models, and processes for transparency and reproducibility.