Join EY and contribute to the expansion of our AI Strategy & Transformation practice. This leadership role blends hands-on cloud and AI platform architecture expertise with consulting acumen. You will drive end-to-end AI platform implementation, infrastructure modernization, and enterprise deployment programs, while also managing key client relationships at the CxO level.

This position requires deep technical skills in AI platform and infrastructure engineering, multi-cloud architecture, automation, and MLOps. Proven program leadership across industries such as BFSI and manufacturing is essential.

Lead the design and implementation of enterprise AI platforms for AI/ML, GenAI, and RAG workloads across hybrid and multi-cloud environments. Architect scalable cloud-native AI foundations using AWS SageMaker, Azure ML, and GCP Vertex AI, alongside Kubernetes platforms. Build secure and resilient infrastructure for AI model development, training, and deployment. Design reusable platform services for model hosting, inference endpoints, and vector databases.

Establish platform observability for AI systems, ensuring centralized logging, monitoring, and cost optimization. Enable secure AI platform controls, supporting Responsible AI principles and compliance. Drive AI platform architecture standardization through reusable patterns and best practices. Serve as a primary technical advisor to CxOs and stakeholders on AI platform strategy and cloud modernization.

A Bachelor's degree in Computer Science, IT, AI, or ML is mandatory, with a Master's in Cloud Computing, AI, Data Engineering, or Machine Learning being preferred. Candidates should 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 environment.

Core technical expertise includes AI platform & infrastructure engineering, deep knowledge of AWS, Azure, and GCP AI services, and strong automation and deployment engineering skills with tools like Terraform and Kubernetes. Experience with MLOps/LLMOps, data engineering, Python, shell scripting, and platform security is also required. Proven track record in leading enterprise AI platform implementations and consulting experience with CxO audiences are key.