Python AI Engineer Automated Test Data Generation

**Urgent Hiring for the Role of Python AI Engineer Automated Test Data Generation for our team**

Title: Python AI Engineer Automated Test Data Generation

Experience: 8+years

Location : Remote

Timing: US EST Hours

Overview

We are seeking a Python AI Engineer with expertise in synthetic and automated test data generation to support clinical trials, regulatory submissions, and GxP-compliant systems across the life sciences domain. This role will focus on building intelligent frameworks that generate high-quality, audit-ready datasets for use in clinical data management (CDM), biostatistics, pharmacovigilance, and regulatory validation environments.

By leveraging AI/ML techniques and Python-based automation, this role will help reduce dependency on production data, accelerate testing cycles, and ensure compliance with stringent regulatory requirements (FDA, EMA, ICH E6, 21 CFR Part 11).

Key Responsibilities

Synthetic Test Data Automation

• Design and implement Python frameworks to automatically generate synthetic clinical and operational datasets.
• Create SDTM- and ADaM-compliant datasets for testing downstream workflows (data transformation, analysis, and reporting).
• Generate domain-specific datasets (e.g., adverse events, lab results, demographics, drug exposure) with variability, edge cases, and referential integrity.
• Develop connectors for Define-XML, Pinnacle21 validation, and statistical programming workflows.

AI/ML Application in Data Generation

• Apply generative AI (LLMs, GANs, diffusion models) to simulate realistic clinical case scenarios.
• Build synthetic patient journeys for eCOA, EDC, CTMS, LIMS, or pharmacovigilance systems.
• Use AI-enabled automation to mimic rare or complex conditions for system stress testing.

Compliance & Data Governance

• Ensure generated data adheres to GxP, 21 CFR Part 11, and HIPAA/GDPR requirements.
• Implement data anonymization/obfuscation while maintaining clinical and statistical relevance.
• Document all processes, including validation protocols, for audit and inspection readiness.

Collaboration & Integration

• Partner with Clinical Data Management, Biostatistics, Pharmacovigilance, and Quality/Compliance teams to understand requirements.
• Integrate test data generation into CI/CD pipelines, clinical trial simulation frameworks, and automated validation environments.
• Support SMEs in assessing AI-generated datasets for plausibility and regulatory acceptability.

Required Skills & Qualifications

• Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow, LangChain, etc.).
• Experience generating synthetic clinical trial data or working with CDISC standards (SDTM, ADaM, Define-XML).
• Familiarity with clinical data management systems (Medidata Rave, Veeva CDB, Oracle InForm, etc.) and testing needs.
• Understanding of GxP/21 CFR Part 11 compliance and regulated data handling.
• Knowledge of test automation frameworks (PyTest, Robot Framework) and data validation tools (e.g., Pinnacle21).

Preferred Qualifications

• Experience supporting clinical trial systems (EDC, CTMS, PV, LIMS, eCOA).
• Understanding of statistical programming workflows (SAS, R) and integration with test data pipelines.
• Hands-on experience with cloud platforms (AWS, Azure, GCP) for secure AI/ML deployment.
• Familiarity with inspection readiness and validation documentation in a pharma context.

Note: Interested candidates can drop their resumes at aagnihotri@fcsltd.com or dsingh15@fcsltd.com