NOIDA · FULLTIME
RC-REGULATORY COMPLIANCE-GxP AI Model Consultant-Manager
Ernst Young
Noida · onsite · Posted 4d ago
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Undisclosed8–13 yrsfulltimeNoida
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Section · 01
About this role
Job Title
EY- GDS Consulting Enterprise Risk (ER) Regulatory Compliance Manager GxP AI/Model Consultant
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 Manager 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.
The opportunity We are seeking a highly experienced and detail-oriented AI Model Consultant with strong expertise in working alongside AI/ML models, Generative AI (GenAI) systems, and multiagent workflows within GxP-regulated and quality-critical environments. The ideal candidate will work on defining flows, integrate APIs and ensure the reliability, fairness, robustness, and regulatory adherence of AI solutions while working alongside cross-functional teams in designing, testing, and maintaining production-ready, inspection-ready AI systems.
This role is responsible for leading AI model consultation and development activities across the full model lifecycle, including model integration, training, testing, deployment, and postdeployment monitoring, in alignment with GxP principles, risk-based validation, and data integrity expectations. The consultant will play a key role in developing Model-based use cases, leveraging 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 - Lead AI and Agentic AI engagements from strategy through implementation and value realization.
- Design and oversee development of AI solutions leveraging LLMs, AI Agents, Multi-Agent Systems, RAG, Knowledge Graphs, and AI Orchestration frameworks.
- Operate in Regulated Environments - Ability to support AI governance, model inventory, periodic review, monitoring controls, change management, traceability, and validation in GxP-regulated environments.
- Design AI Validation and Testing Strategies - Experience creating risk-based test strategies, evaluation datasets, adversarial testing scenarios, edge-case testing, and validation evidence.
- Apply Explainability and Responsible AI Practices - Understanding of explainability concepts, bias assessment, transparency requirements, auditability, and AI risk controls.
- Design and execute testing strategies for AI agents and multiagent workflows, covering orchestration logic, tool invocation, and output validation.
- Collaborate with data scientists and engineers to identify model improvement opportunities and risk mitigations.
- Drive client workshops to identify AI use cases, define target operating models, and develop AI roadmaps.
- 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.
- Bachelors or masters degree in Life Sciences, Engineering, or related field.
- 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.
- Exposure to cloud ecosystems such as Azure, AWS, or GCP.
- Consulting or client-facing experience preferred.
- Excellent communication, documentation, and stakeholder management skills.
- 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.
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Section · 02
Skills
apisclusteringai modelamazon web servicesclassificationChange ControlChange Managementai governanceAnomaly DetectionCapital MarketsBusiness DevelopmentcloudCommunication SkillsBusiness TransformationcomplianceAi SolutionsMicrosoft AzureArtificial Intelligence
Section · Company
About Ernst & Young
Ernst Young
Management Consulting
44k+
employees
2002
24 years old
Gurgaon / Gurugram, Haryana
India
₹15.3L PA avg
Avg at Ernst & Young
About
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