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Tier-1 employer and metro location increase applicant density despite specialized GenAI skill requirements.
Strong pharma/regulatory and semantic data emphasis requires domain-specific experience, reducing cross-industry transferability.
Explicit 7–12 years requirement plus many mandatory GenAI, cloud, Databricks, and knowledge-graph skills.
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Lead design and delivery of AI solutions leveraging Generative AI, agentic AI, semantic data, and enterprise data platforms to drive measurable business impact in pharmaceutical and operational domains.
Oversee end-to-end lifecycle of AI projects including ideation, prototyping, production deployment, ensuring alignment with Responsible AI, compliance, and data governance standards.
Collaborate with business stakeholders, product teams, and technology partners to execute AI-enabled transformation initiatives, particularly within regulatory, operational, and data-centric use cases.
7 to 12 years of experience in Data Science, AI, Computer Science, or related field with a Master’s or Bachelor’s degree.
Proficiency in Python and SQL for AI development and enterprise data integration.
Experience with AWS cloud services (including AWS Bedrock), OpenAI APIs, Databricks, and knowledge graph technologies (e.g., Stardog, GraphDB).
Strong understanding of Responsible AI principles, governance, compliance requirements, and familiarity with AI regulatory considerations such as the EU AI Act.
Proven ability to translate complex business strategies into scalable AI solutions in a regulated environment, especially within pharmaceutical or life sciences sectors.
Experience leading cross-functional teams and managing stakeholder relationships to drive AI strategy and enterprise transformation.
Technical depth in Generative AI, large language models, semantic interoperability, and enterprise data architectures with a product- and outcome-oriented mindset.