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Strong employer brand, metro location, and broad platform requirements increase applicant competition.
Technical skills are transferable but pharmaceutical regulatory and governance requirements raise domain sensitivity.
Senior technical leadership, regulated industry experience, and specific platform requirements produce strict shortlisting.
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Define and lead enterprise AI architecture strategy focusing on agentic AI, foundation models, and scalable AI platforms across AWS, OpenAI, and hybrid environments.
Establish enterprise-wide AI standards, reference architectures, and ensure alignment with data governance, security, and pharmaceutical regulations.
Collaborate cross-functionally to translate AI strategy into scalable solutions, oversee AI integration, and lead architectural oversight in large transformation programs.
Bachelor’s degree or equivalent experience in data science, AI engineering, or related field.
Hands-on experience leading enterprise AI platform architecture and delivering practical architectural solutions.
Experience with AI platforms such as Amazon Bedrock, Amazon Q, SageMaker, Azure AI, or Google Cloud AI, including hybrid cloud/containerization strategies.
Experience defining and applying information and data governance standards in regulated environments (e.g., pharmaceutical).
Senior AI architect with proven experience delivering end-to-end AI architecture blueprints at enterprise scale, especially with agentic AI and multi-agent orchestration.
Expertise bridging data science, data engineering, and enterprise architecture, capable of aligning technology choices with business and regulatory requirements.
Experience working in regulated industries like pharmaceuticals, with strong knowledge of AI governance, security, and compliance frameworks.