EY - GDS Consulting - AI and DATA -...

Senior AI Engineer – Responsible AI

Join EY's Global Delivery Services (GDS) Consulting team in Bengaluru as a Senior AI Engineer specializing in Responsible AI (RAI). In this role, you will be instrumental in integrating ethical AI principles throughout the entire AI/ML lifecycle, from initial project conception to final deployment. You will leverage your solid understanding of cloud platforms like Azure or AWS to champion responsible AI practices, assist enterprises in mitigating AI-related risks, and drive the adoption of Responsible AI solutions.

Your responsibilities will include building automated Responsible AI guardrails, streamlining complex AI concepts, detecting and mitigating AI system risks, ensuring compliance with data privacy standards (e.g., GDPR, CCPA, HIPAA), and strengthening our service offerings. This position demands hands-on expertise in monitoring, observability, MLOps, and comprehensive AI/ML lifecycle management.

Key Responsibilities:

• Embed Responsible AI principles (fairness, transparency, accountability) across the AI/ML lifecycle.
• Design and implement automated Responsible AI guardrails for real-time monitoring of fairness, explainability, robustness, and safety.
• Develop and enforce Responsible AI metrics using frameworks like Fairlearn, AI Fairness 360, SHAP, and LangChain integrations.
• Define and propagate best practices for Responsible AI adoption, including model interpretability and secure data handling.
• Establish monitoring pipelines using tools such as Arize AI, MLflow, and Langfuse to track model performance, bias, and drift in real-time.
• Develop observability solutions to enhance AI/ML lifecycle management, ensuring explainable, reliable, and secure models.
• Deploy Responsible AI tools and frameworks to cloud platforms (Azure, AWS) using orchestration tools like Kubernetes and Docker.
• Build dashboards and analytics for actionable insights into AI system behavior.
• Implement Responsible AI measures in Agentic architectures and govern multi-agent systems including autonomous LLM agents.
• Incorporate LLM-based evaluation mechanisms to assess model outputs for ethical and quality adherence.
• Lead teams in monitoring emerging AI trends and advancements in Responsible AI.
• Drive innovation by implementing scalable and ethical AI solutions.
• Mentor junior engineers on ethical AI development and knowledge sharing.
• Develop frameworks for reproducible, transparent, and compliant AI solutions.
• Collaborate with cross-functional teams to integrate Responsible AI principles into AI Ops and MLOps pipelines.
• Conduct regular technical audits of AI/ML and LLM-based systems to detect and mitigate issues like bias, drift, and hallucinations.
• Ensure robust monitoring systems are in place to address AI risks and maintain regulatory compliance.

Required Skills and Experience:

• Minimum of 5+ years in AI/ML development, with at least 1+ year focused on implementing Responsible AI principles.
• Expertise in bias detection, explainability, drift analysis, and regulatory standards (GDPR, CCPA, HIPAA).
• Proficiency in Responsible AI frameworks and tools (Fairlearn, AI Fairness 360, SHAP, LangChain).
• Strong knowledge of AI risk mitigation strategies and automated guardrails.
• Advanced hands-on experience with Python, FastAPI, and Flask.
• Proven experience with cloud platforms (Azure, AWS) and containerization/orchestration (Kubernetes, Docker).
• Extensive experience in AI/ML model lifecycle management and monitoring tools (MLflow, Arize AI, Langfuse).
• Expertise in building and managing MLOps pipelines with integrated Responsible AI principles.
• Strong problem-solving, analytical skills, and experience in design thinking and architecture creation.
• Experience with multi-agent systems and agentic architectures.
• Knowledge of emerging AI governance trends and LLM-based evaluation.
• Excellent communication and collaboration skills, with experience mentoring junior engineers.

Good to Have Skills:

• Proficiency in design thinking and scalable AI architectures.
• Familiarity with DevOps practices and CI/CD pipelines for AI/ML.
• Hands-on experience with advanced AI/ML model fine-tuning and optimization.
• Exposure to frameworks like LangChain and Llamaindex.