AI Agent Developer – Statistical Programming / SDTM Automation

W2 only - no 3rd party candidates Seeking an experienced Statistical Programmer with hands-on experience developing AI agent-based applications to support SDTM automation initiatives. This role will contribute to the design and implementation of AI-driven solutions that streamline SDTM mapping, validation, metadata generation, and regulatory submission processes. The ideal consultant will have knowledge of SDTM and clinical data standards with proven hands-on experience developing and deploying AI agent applications. They should be able to demonstrate examples of AI agents they have personally built that automate clinical data review, SDTM mapping, validation, metadata generation, or related Statistical Programming workflows. Key Responsibilities • Design and develop AI agent applications to automate SDTM programming and data standardization activities. • Build solutions that leverage LLMs, agent frameworks, and workflow orchestration to support clinical data transformation and validation. • Develop and maintain automation for SDTM dataset creation, quality checks, metadata review, and submission readiness activities. • Apply CDISC standards including SDTM, CDASH, and Define.xml within AI-enabled workflows. • Partner with Statistical Programmers and Data Standards teams to identify automation opportunities. • Implement auditability, validation, and compliance controls suitable for regulated environments. • Evaluate and optimize AI agent performance, accuracy, and reliability. Required Qualifications • 5+ years of life science Statistical Programming experience and BS/MS in related field • Strong knowledge of CDISC SDTM and regulatory submission requirements. • Hands-on experience building AI agent-based applications using frameworks (examples include LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or similar.) • Strong Python programming skills. • Experience integrating LLMs into business workflows and automation solutions. • Experience with RAG architectures, prompt engineering, and AI workflow orchestration. • Understanding of GxP and clinical development processes.