Join EY and contribute to building a better working world. As a Manager within EY's GDS Consulting – Enterprise Risk (ER) – Regulatory Compliance team, you will be instrumental in guiding clients through complex business needs and delivering impactful solutions. This role focuses on establishing, maintaining, and strengthening client relationships while ensuring high-quality deliverables and operational efficiency. You will proactively identify and escalate risks to both clients and EY senior management, while also driving internal initiatives.

This position is ideal for a highly experienced AI Model Consultant with specialized expertise in AI/ML, Generative AI (GenAI), and multi-agent workflows within GxP-regulated and quality-critical sectors. The role involves defining operational flows, integrating APIs, and ensuring the reliability, fairness, and regulatory compliance of AI solutions. Collaboration with cross-functional teams is essential for designing, testing, and maintaining production-ready, inspection-ready AI systems.

The consultant will lead AI model consultation and development across the entire lifecycle, from integration and training to deployment and post-deployment monitoring. This includes adhering to GxP principles, risk-based validation, and data integrity standards. Key contributions will be in developing Model-based use cases, employing quality-by-design and governance-by-design methodologies to enable the safe, trustworthy, and explainable deployment of AI and agent-based solutions in life sciences and pharmaceutical industries.

Lead AI and Agentic AI engagements from strategy through implementation. Design and oversee the development of AI solutions using LLMs, AI Agents, Multi-Agent Systems, RAG, Knowledge Graphs, and AI Orchestration frameworks. Facilitate client workshops to pinpoint AI use cases, define target operating models, and create AI roadmaps. Manage diverse teams including AI Engineers, Data Scientists, Architects, and Business Consultants. Establish robust AI governance, observability, risk management, model monitoring, and responsible AI practices. Support business development efforts, including proposal creation, solutioning, thought leadership, and client presentations. Conduct AI/ML model integration, evaluation, and validation across all development, training, testing, and deployment stages. Evaluate model performance metrics such as precision, recall, accuracy, F1-score, and confusion matrices. Analyze business requirements to translate them into functional and technical solution designs. Configure, customize, and develop applications, workflows, data models, integrations, and reports tailored to specific business needs. Assess model behavior across various datasets, edge cases, and failure scenarios to identify bias, drift, and instability. Demonstrate a solid understanding of supervised and unsupervised learning models, including classification, regression, clustering, and anomaly detection. Perform hands-on evaluations of GenAI and LLM-based systems, focusing on prompt behavior, response quality, and consistency. Design and execute comprehensive testing strategies for AI agents and multi-agent workflows, ensuring proper orchestration logic, tool invocation, and output validation. Leverage LangChain, LangGraph, and Langfuse for agent workflows, observability, traceability, and evaluation logging. Collaborate with data scientists and engineers to identify model improvement opportunities and implement risk mitigations.

A Bachelor’s or Master’s degree in Life Sciences, Engineering, or a related discipline is required. You should possess at least 8 years of experience in AI/ML, GenAI, software testing, validation, or quality engineering. Proven experience in creating user stories, test scripts, validation plans, or AI solution prototypes is essential. Familiarity with cloud ecosystems like Azure, AWS, or GCP is beneficial. Consulting or client-facing experience is preferred. Excellent communication, documentation, and stakeholder management skills are crucial for success in this role.

Key skills include a strong understanding of ML models, LLMs, GenAI workflows, agentic systems, and evaluation techniques, alongside a solid foundation in machine learning concepts. Practical, hands-on experience with Precision, Recall, Confusion Matrix, ROC/AUC, and both Supervised and Unsupervised ML models is expected. Proficiency with LangChain, LangGraph, and Langfuse is required for agent workflows and evaluation. Experience evaluating GenAI/LLM-based applications and agents, along with strong documentation, analytical, and stakeholder communication skills, are vital.

You must be adept at designing test cases, user stories, and acceptance criteria for AI/ML systems. The ability to validate AI outputs, assess accuracy, detect bias, and evaluate model reliability under diverse conditions is key. Hands-on skills in building prototypes, test harnesses, and demo environments are necessary. Strong stakeholder management, analytical thinking, and structured communication are essential attributes. Familiarity with version control, prompt engineering fundamentals, and AI-assisted testing tools is preferred. A thorough understanding of data integrity principles and electronic records/e-signatures compliance is also important. Experience in client-facing roles, managing expectations and delivering solutions, alongside strong communication and presentation skills, is highly valued. The capacity to troubleshoot application issues and recommend effective solutions, coupled with a strong teamwork and collaboration mindset, will contribute to your success.