GDS Consulting_AI Engineering and Machine Learning Manager

EY GDS - AI & Data

Manager - AI/ML Transformation & Delivery

Role Overview

EY GDS is seeking an experienced AI/ML Manager to lead enterprise AI transformation initiatives, drive innovation, and deliver scalable AI solutions that create measurable business value. This role combines AI strategy, solution architecture, delivery leadership, people management, and stakeholder engagement across Machine Learning, Generative AI, and Intelligent Automation programs.

The ideal candidate brings 8-11 years of experience in Data Science, Machine Learning, and AI, with deep expertise in Generative AI, LLMs, AI Agents, RAG architectures, MLOps, and Cloud AI platforms. You will be responsible for shaping AI roadmaps, leading high-performing teams, and enabling organizations to accelerate AI adoption at scale.

Key Responsibilities

AI Strategy & Business Transformation

• Define and execute AI strategies aligned with business objectives and digital transformation goals.
• Identify, prioritize, and deliver AI use cases that drive measurable business outcomes, operational efficiency, and competitive advantage.
• Partner with business leaders, clients, and stakeholders to translate strategic priorities into scalable AI solutions.
• Serve as a trusted advisor on AI adoption, governance, and enterprise AI operating models.

AI Solution Architecture & Delivery

• Own the architecture, design, and technical direction of enterprise AI platforms and solutions.
• Lead the implementation of Machine Learning, Generative AI, AI Agents, and Retrieval-Augmented Generation (RAG) solutions.
• Establish scalable and secure AI architectures leveraging Azure AI, OpenAI, Databricks, and modern cloud ecosystems.
• Drive delivery excellence across multiple AI engagements and programs.

AI Engineering & MLOps

• Establish enterprise MLOps practices covering model development, deployment, monitoring, governance, and lifecycle management.
• Lead implementation of CI/CD pipelines and AI platform automation.
• Drive adoption of modern AI engineering frameworks, including MLflow, Docker, Kubernetes, and Infrastructure-as-Code practices.
• Ensure AI solutions are reliable, scalable, secure, and production-ready.

Leadership & Talent Development

• Lead, coach, and mentor teams of Data Scientists, ML Engineers, and AI Engineers across multiple engagements.
• Build high-performing teams through technical leadership, capability development, and career coaching.
• Support workforce planning, resource management, and competency growth initiatives.
• Foster a culture of innovation, continuous learning, and engineering excellence.

Responsible AI & Governance

• Champion Responsible AI practices, including fairness, explainability, transparency, security, privacy, and compliance.
• Establish governance frameworks for enterprise AI and Generative AI implementations.
• Ensure AI solutions meet regulatory, ethical, and organizational standards.

Practice Growth & Innovation

• Contribute to business development, solutioning, proposals, and AI thought leadership initiatives.
• Evaluate emerging AI technologies and drive adoption of innovative capabilities across the practice.
• Support the growth of EY’s AI & Data capabilities through reusable assets, accelerators, and best practices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, AI or a related field.
• 8-11 years of experience in Artificial Intelligence, Machine Learning, Data Science, or Advanced Analytics.
• Proven experience leading enterprise AI/ML programs and managing technical teams.
• Strong expertise in Python and SQL.
• Hands-on experience with Machine Learning, Deep Learning, Generative AI, NLP, LLMs, AI Agents, and RAG architectures.
• Experience architecting and deploying AI solutions on Azure, AWS, or GCP.
• Strong understanding of AI platform architecture, model governance, and MLOps best practices.
• Excellent leadership, stakeholder management, consulting, and communication skills.
• Demonstrated ability to translate business challenges into AI-driven outcomes and value realization.

Preferred Qualifications

• Experience with Azure OpenAI, Azure AI Foundry, Azure Machine Learning, Databricks, MLflow, and Spark.
• Knowledge of vector databases, semantic search, knowledge engineering, and agentic AI frameworks.
• Experience driving AI governance, model risk management, and Responsible AI initiatives.
• Experience leading global delivery teams and large-scale transformation programs.
• Relevant Microsoft Azure, Databricks, AWS, or GCP certifications.

What Success Looks Like

A successful AI/ML Manager is a business-focused AI leader who combines strategic thinking, technical depth, and people leadership to:

• Deliver enterprise-scale AI solutions that generate measurable business impact.
• Build and lead high-performing AI teams.
• Drive AI adoption across business functions and client engagements.
• Establish scalable AI platforms, governance, and engineering standards.
• Contribute to the growth and market leadership of the AI & Data practice.

This role is ideal for leaders who can bridge business strategy, AI innovation, and delivery execution while developing the next generation of AI talent.