Job Summary

At EY, you'll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we're counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

The Opportunity

As part of our EY-ER- Regulatory Compliance team, you will help the clients by understanding their business needs and delivering solutions in accordance with the EY guidelines & methodologies. As a Regulatory Compliance senior, actively establish, maintain and strengthen internal and external relationships. In line with our commitment to quality, you'll consistently drive projects to completion with high-quality deliverables, achieve operational efficiency, proactively identify and raise risks with the client as well as EY senior management team and lead internal initiatives.

This role is responsible for leading AI model consultation and development activities across the full model lifecycle, including model integration, training, testing, deployment, and post-deployment monitoring, in alignment with GxP principles, risk-based validation, and data integrity expectations. The consultant will play a key role in embedding Responsible AI, quality-by-design, and governance-by-design practices, enabling the safe, trustworthy, and explainable use of AI and agent-based solutions in regulated life sciences and pharmaceutical contexts.

Your key responsibilities

- Work on development of AI/ML and Agentic AI strategies by providing analysis, validation insights, and readiness assessments.
- Perform AI/ML model integration, evaluation and validation across development, training, testing, and deployment phases.
- Collaborate with data scientists and engineers to identify model improvement opportunities and risk mitigations.
- Design and execute testing strategies for AI agents and multiagent workflows, covering orchestration logic, tool invocation, and output validation.
- Document evaluation results, limitations,



risks, and acceptance criteria in a clear, audit-ready manner.
- Develop or review validation plans, user and functional requirements, risk assessments, requirements traceability matrices, test protocols, test evidence, deviation records, and validation summary reports.
- Establish acceptance criteria for data quality and model performance, including accuracy, sensitivity, specificity, precision, recall, F1 score, calibration, error rates, and other measures appropriate to the use case.
- Execute or independently review functional, integration, regression, negative, boundary, security-role, audit-trail, data-reconciliation, and user-acceptance testing for AI-enabled workflows.
- Challenge model reliability through reproducibility, robustness, bias, subgroup-performance, explainability, stress, and edge-case testing, with clear documentation of limitations and residual risk.
- Define production monitoring for model performance, data and concept drift, anomalous outputs, override patterns, human review, complaints, incidents, and other signals that may trigger investigation or revalidation.
- Evaluate proposed model updates, retraining, prompt changes, knowledge-base changes, infrastructure changes, and vendor releases through formal change control and impact assessment.
- Ability to perform root cause analysis for hallucinations, inaccurate outputs, agent failures, latency issues, and unexpected model behaviour.
- Assess model behavior across datasets, edge cases, and failure scenarios to identify bias, drift, and instability.
- Understand ML Fundamentals - Strong grasp of classification, regression, clustering, anomaly detection, and model performance metrics (Precision, Recall, F1,



AUC).
- Evaluate LLM and GenAI Systems - Experience measuring accuracy, completeness, relevance, groundedness, faithfulness, consistency, and task completion.
- Analyze Drift and Model Performance - Ability to identify and investigate data drift, concept drift, model drift, and performance degradation in production environments.
- Assess RAG-Based Solutions - Understand retrieval pipelines and be able to evaluate retrieval quality, citation accuracy, and response grounding.
- Work with AI Observability Platforms - Experience with AI monitoring, tracing, evaluation logging, dashboards, and observability tools such as Langfuse, LangSmith, Arize, WhyLabs, MLflow, or similar platforms.
- Use LangChain, LangGraph, and Langfuse for agent workflows, observability, traceability, and evaluation logging.
- Implement and use AI observability capabilities for tracing, evaluation logging, dashboards, alerting, failure analysis, and audit-ready evidence across model and agent workflows.

Qualifications
- Bachelors or masters degree in Life Sciences, Engineering, or related field.
- 5+ 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.
- Exposure to cloud ecosystems such as Azure, AWS, or GCP.
- Consulting or client-facing experience preferred.
- Excellent communication, documentation, and stakeholder management skills.

Must-Have Skills And Attributes
- Strong understanding of ML models, LLMs, GenAI workflows, agentic systems, and evaluation techniques.
- Strong foundation in machine learning concepts and model evaluation techniques.
- Hands-on experience with: Precision, Recall, Confusion Matrix, ROC/AUC, Supervised and Unsupervised ML models
- Practical experience with LangChain, LangGraph, and Langfuse.
- Experience evaluating GenAI / LLM-based applications and agents.
- Robust documentation, analytical, and stakeholder communication skills.

📌 RC-REGULATORY COMPLIANCE-GxP AI Model Consultant-Senior (Hyderabad)
🏢 EY
📍 Hyderabad