Join EY's Global Delivery Services as a Senior Data and AI Engineering professional. This role focuses on designing, building, and operating cloud-native data platforms and AI/ML solutions, primarily leveraging Google Cloud. You will work on impactful industry use cases, integrating scalable data engineering with Vertex AI, GenAI-enabled automation, and token-aware solutions to modernize enterprise data platforms and accelerate insight generation. Collaboration with cross-functional teams is key to building robust pipelines, integrating GenAI into data workflows, and ensuring cost-efficient, production-grade deployments.

Design and optimize scalable data pipelines and ETL workflows using Python, Java, Go, or Scala on platforms like Apache Spark and Google Cloud Dataflow. Develop cloud-native AI/ML solutions with Vertex AI for various industry use cases, focusing on data flow, model lifecycle, and measurable outcomes. Integrate GenAI capabilities into data pipelines for enhanced automation, insight generation, and intelligent data processing. Apply token economics principles for GenAI solutions, optimizing prompts, monitoring usage, and managing costs. Stay updated on emerging AI/GenAI technologies and implement them in modern data platforms. Design reusable solution patterns for Google Cloud and adapt them for Microsoft Azure as needed. Develop and maintain application components and automation scripts, manage container lifecycles, and implement CI/CD pipelines. Document technical knowledge and procedures comprehensively.

Requires strong hands-on experience in data engineering and cloud-native engineering on Google Cloud, with practical experience in Vertex AI. Proficiency in building and optimizing ETL/data pipelines using Python, Java, Go, or Scala, and executing workloads on Spark and Google Cloud Dataflow is essential. A working knowledge of GenAI integration patterns and token economics for GenAI solutions is expected. Familiarity with multi-cloud deployment patterns, particularly translating architectures between Google Cloud and Microsoft Azure, is necessary. Strong programming skills in Java, Python, Go, and scripting languages are required, along with experience in application and container lifecycle management and CI/CD pipeline setup. A Professional or Practitioner-level Google Cloud certification is mandatory, demonstrating hands-on public cloud delivery experience. Excellent analytical thinking, problem-solving abilities, and clear communication skills are crucial for collaborating with diverse teams.