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20 hours ago
We are seeking a Python AI Engineer to design and implement intelligent frameworks for synthetic test data generation in support of clinical trials, regulatory submissions, and GxP-compliant systems.
Job OverviewThis role will leverage AI/ML and Python automation to deliver high-quality, audit-ready datasets that reduce reliance on production data, accelerate testing cycles, and ensure compliance with FDA, EMA, and global regulatory standards.
Key Responsibilities- Data Generation: Build Python frameworks to generate synthetic clinical and operational datasets.
- Dataset Delivery: Produce CDISC-compliant datasets (SDTM, ADaM) for data transformation, analysis, and reporting workflows.
- Domain Expertise: Simulate domain-specific datasets (adverse events, labs, demographics, drug exposure) with variability and referential integrity.
- Generative AI: Apply generative AI (LLMs, GANs, diffusion models) to model realistic clinical scenarios.
- Patient Journey Simulation: Create synthetic patient journeys across EDC, eCOA, CTMS, LIMS, and PV systems.
- AI-Driven Automation: Use AI-driven automation to simulate rare or complex cases for stress and edge-case testing.
- Regulatory Compliance: Ensure adherence to GxP, 21 CFR Part 11, HIPAA, and GDPR standards.
- Data Anonymization: Implement anonymization/obfuscation while maintaining clinical and statistical fidelity.
- Validation Protocols: Produce validation protocols and documentation for audit and inspection readiness.
- Team Collaboration: Work with Clinical Data Management, Biostatistics, Pharmacovigilance, and Compliance teams to gather requirements.
- Process Embedment: Embed test data generation into CI/CD pipelines, trial simulations, and automated validation environments.
- SME Validation: Partner with SMEs to validate AI-generated datasets for regulatory acceptability.
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