• Serve as a key technical and strategic contributor to Syneos Health’s Safety & Pharmacovigilance (PV) AI enablement initiatives • Explore, develop, and implement artificial intelligence (AI), machine learning (ML), and automation solutions to enhance pharmacovigilance processes • Design, build, and test AI/ML-based prototypes and proof-of-concept solutions for PV workflows • Develop and optimize prompt engineering strategies for large language models (LLMs) • Evaluate and integrate AI technologies and APIs into existing PV systems or processes • Collaborate with developers and data engineers to ensure scalable, compliant implementation of AI tools • Support model evaluation, validation, and monitoring activities to ensure accuracy, reliability, and compliance with regulatory standards • Work cross-functionally with Safety Operations, Data Science, IT, and Business Analysts to align AI initiatives with PV business needs

• Act as a solution expert across ML domains including evaluations, training, inference, data pipelines, quality, and optimisation • Work directly alongside product teams as a trusted partner, helping them navigate technical challenges and arrive at effective solutions • Provide expert guidance on platform capabilities, helping teams understand what's possible and how to get there efficiently • Develop blueprints, patterns, and paved roads that allow other teams to follow proven approaches and accelerate their own implementations • Debug and resolve complex issues, identifying root causes and sharing learnings broadly • Balance hands-on problem solving with strategic thinking about how to scale enablement impact across the organisation

• Act as a solution expert across ML domains including evaluations, training, inference, data pipelines, quality, and optimisation • Work directly alongside product teams as a trusted partner, helping them navigate technical challenges and arrive at effective solutions • Provide expert guidance on platform capabilities, helping teams understand what's possible and how to get there efficiently • Develop blueprints, patterns, and paved roads that allow other teams to follow proven approaches and accelerate their own implementations • Debug and resolve complex issues, identifying root causes and sharing learnings broadly • Balance hands-on problem solving with strategic thinking about how to scale enablement impact across the organisation

• Improve our existing machine learning ranking models using your core coding skills and ML knowledge • Identify new opportunities to apply machine learning to different parts of the Quora product • Work with other machine learning engineers to implement algorithms and systems efficiently • Take end-to-end ownership of machine learning systems - data pipelines, candidate extraction, feature engineering, model training, as well as integration into our production systems