Join EY's GDS Consulting team as a Regulatory Compliance Manager, specializing in GxP AI/Model Consultation. You will guide clients in understanding their business needs and implementing solutions aligned with EY's methodologies.

This role focuses on AI Model Consulting within GxP-regulated environments, emphasizing Generative AI, multi-agent workflows, and ensuring the reliability, fairness, and regulatory adherence of AI solutions. You will lead the full AI model lifecycle, from integration and training to deployment and monitoring, all while adhering to GxP principles and data integrity standards.

Develop AI-based use cases and leverage quality and governance-by-design practices to enable the safe, trustworthy, and explainable use of AI in regulated life sciences and pharmaceutical sectors.

Lead AI and Agentic AI engagements from strategy to implementation and value realization. Design and oversee AI solutions using LLMs, AI Agents, Multi-Agent Systems, RAG, Knowledge Graphs, and AI Orchestration frameworks.

Operate within regulated environments by supporting AI governance, model inventory, periodic reviews, monitoring controls, change management, traceability, and validation. Develop risk-based test strategies, design evaluation datasets, and implement adversarial and edge-case testing for AI solutions.

Apply explainability and responsible AI practices, focusing on transparency, auditability, and AI risk controls. Design and execute testing for AI agents and multi-agent workflows, including orchestration logic and output validation. Collaborate with data scientists and engineers to identify model improvements and risk mitigations.

Drive client workshops to define AI use cases, operating models, and roadmaps. Establish acceptance criteria for data quality and model performance, and execute various testing types, including functional, integration, regression, and user-acceptance testing.

Challenge model reliability through rigorous testing and documentation of limitations. Define production monitoring for model performance, drift, and anomalous outputs. Evaluate proposed model updates and changes through formal change control processes and conduct root cause analysis for AI system failures.

Possess a Bachelor's or Master's degree in Life Sciences, Engineering, or a related field. Requires 8+ years of experience in AI/ML, GenAI, software testing, validation, or quality engineering roles.

Experience creating user stories, test scripts, validation plans, or AI solution prototypes is essential. Exposure to cloud ecosystems such as Azure, AWS, or GCP is beneficial. Consulting or client-facing experience is preferred.

Key skills include a strong understanding of ML models, LLMs, GenAI workflows, agentic systems, and evaluation techniques. Practical experience with LangChain, LangGraph, and Langfuse is required, along with experience evaluating GenAI/LLM-based applications and agents.

Demonstrate strong documentation, analytical, and stakeholder communication skills. Experience designing test cases, user stories, and acceptance criteria for AI/ML systems is crucial. The ability to validate AI outputs, evaluate accuracy, detect bias, and assess model reliability is paramount.

Hands-on skills in building prototypes and test harnesses are valued. Familiarity with version control, prompt engineering fundamentals, and AI-assisted testing tools is preferred. A strong understanding of data integrity principles and electronic records/e-signatures compliance is necessary. Excellent communication and presentation skills are required, along with the ability to troubleshoot application issues.