EY is seeking a seasoned Manager to lead and expand its AI Strategy & Transformation practice. This role requires a blend of hands-on cloud and AI platform engineering expertise with strategic consulting acumen. You will spearhead end-to-end AI platform implementations, infrastructure modernization, and enterprise deployment programs, while managing key client relationships at the CxO level.

The ideal candidate possesses deep technical skills in AI platform engineering, multi-cloud architecture, automation, MLOps, and the operationalization of AI workloads. Proven leadership experience across diverse industries such as BFSI, manufacturing, and regulated sectors is essential for success in this pivotal position.

Lead the design and implementation of enterprise AI platforms for AI/ML, GenAI, Agentic AI, and RAG workloads across hybrid and multi-cloud environments. Architect scalable cloud-native foundations using AWS SageMaker/Bedrock, Azure ML/OpenAI, and GCP Vertex AI. Build secure, resilient infrastructure for AI model development, training, and deployment, including compute, GPU, storage, and networking.

Establish reusable platform services for model hosting, inference, vector databases, and prompt orchestration. Implement robust observability with centralized logging, monitoring, and performance diagnostics. Drive standardization through reusable patterns and enterprise engineering best practices, ensuring secure AI platform controls and support for Responsible AI principles.

Requires a B.Tech/B.E. in Computer Science, IT, AI, or ML; M.Tech/MS in Cloud Computing, AI, Data Engineering, or Machine Learning is preferred. Possess 10-13 years of experience in cloud platform engineering, AI platform engineering, DevOps, MLOps, or AI consulting, ideally within a Big 4 or tech company.

Core technical expertise includes strong hands-on experience with enterprise AI platforms, cloud services (AWS, Azure, GCP), automation tools (Terraform, Bicep), CI/CD, containerization (Docker, Kubernetes), MLOps/LLMOps tools, and data engineering concepts. Proficiency in Python, shell scripting, and API development is mandatory. Experience with regulated industry deployments and multi-cloud architectures is highly valued.