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Tier-1 brand, metro location, and popular AI/data title increase applicant competition despite specialization.
Specialized generative AI and regulated pharmaceutical use-cases increase domain bias, though core AI skills remain transferable.
Explicit 7–12 years plus specific GenAI, platform, and regulatory requirements make shortlisting highly selective.
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Lead design and delivery of enterprise-grade AI solutions using Generative AI, agentic AI, semantic technologies, and integrated data platforms to drive operations transformation.
Oversee end-to-end AI solution lifecycle from ideation to scalable production deployment, ensuring alignment with Responsible AI and compliance standards in a regulated pharmaceutical context.
Collaborate cross-functionally to align AI strategy with business goals, promote FAIR data principles, and embed AI best practices and governance across enterprise data ecosystems.
Master's or Bachelor's degree in Data Science, AI, Computer Science, Information Science, or related field with 7 to 12 years of relevant experience.
Proficiency in Python for AI development and SQL for data integration.
Experience with AWS cloud services, including AWS Bedrock, OpenAI APIs, and data platforms such as Databricks.
Deep expertise in Generative AI, large language models, agentic AI, knowledge graph technologies, and Responsible AI governance; familiarity with regulatory frameworks like the EU AI Act.
Experienced in driving AI transformation within regulated environments, preferably pharmaceuticals or life sciences with exposure to regulatory and operational data domains.
Capable of balancing deep technical expertise with strategic product thinking and execution accountability.
Proven ability to influence cross-functional teams and lead initiatives that translate complex business strategies into scalable AI solutions aligned with enterprise governance and compliance.