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Niche regulated LLM expertise and seniority reduce qualified applicant density.
Strong regulated-pharma and pharmacovigilance domain requirements make cross-industry transferability low.
Explicit 7+ years, mandatory LLM/tool experience, and GxP/regulatory requirements make filtering highly strict.
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Lead design and implementation of a multi-agent AI platform to transform adverse event data processing in pharmacovigilance.
Own prompt engineering, AI agent orchestration, secure enterprise integration, and validation aligned with regulatory compliance (GxP, GAMP5).
Drive evaluation framework development, CI/CD and MLOps strategies, and technical documentation to improve drug safety operations accuracy and efficiency.
7+ years experience building ML/AI production systems, with at least 2 years working on large language model applications.
Strong expertise with Anthropic Claude APIs, AWS Bedrock and related services, multi-agent LLM orchestration frameworks, and Python software engineering.
Bachelor's degree in computer science or related field, or equivalent professional experience.
Experience designing LLM evaluation frameworks, prompt engineering, and compliance with regulated software environment standards.
Experienced in architecting and deploying AI systems within regulated environments, especially pharmacovigilance or healthcare sectors.
Skilled in collaborating cross-functionally with regulatory, QA, and domain experts to align AI solutions with compliance and safety standards.
Strong capability to communicate complex AI technical concepts clearly to non-technical and regulatory stakeholders, influencing technical direction and governance.