EY GDS is seeking a Senior Data and AI Engineering professional to architect, develop, and manage cloud-native data platforms and AI/ML solutions, with a primary focus on Google Cloud. This role involves leveraging industry-leading use cases that integrate scalable data engineering, Vertex AI-driven machine learning, and GenAI for enhanced automation and token-aware solution design. The objective is to modernize enterprise data platforms and accelerate the generation of actionable insights for clients.

You will collaborate with diverse, cross-functional teams to construct robust data pipelines, embed GenAI capabilities into data workflows, and optimize for token usage and cost-effectiveness. Production-grade deployments will be supported using modern engineering, containerization, and CI/CD practices. While Google Cloud is the primary platform, a strong understanding of deploying similar data engineering, AI/ML, and GenAI solutions on Microsoft Azure is also essential, especially for client or enterprise architecture requirements.

Design and build scalable data pipelines and ETL workflows using Python, Java, Go, or Scala, deploying them on platforms like Apache Flink, Apache Spark, and Google Cloud Dataflow, with a preference for Dataflow expertise. Develop cloud-native AI/ML solutions using Vertex AI for specific industry use cases, detailing the business context, data flow, model lifecycle, and measurable outcomes.

Integrate GenAI capabilities into data pipelines to boost automation, insight generation, predictive analytics, and intelligent data processing across enterprise systems. Apply token economics principles in GenAI solution design, focusing on prompt optimization, usage monitoring, cost control, and performance trade-offs. Stay abreast of emerging AI and GenAI technologies, identifying practical applications for embedding intelligence into data platforms.

Create reusable solution patterns for data engineering, AI/ML, and GenAI workloads, prioritizing Google Cloud implementation and adapting for Microsoft Azure as needed. Develop and maintain application components and automation scripts in Java, Python, Go, and scripting languages. Manage the full application and container lifecycle, from build and packaging to deployment, monitoring, and support.

Implement strong public cloud engineering practices with hands-on Google Cloud experience, requiring a Professional or Practitioner GCP certification. Demonstrate working knowledge of Microsoft Azure services to support the deployment of similar data, AI/ML, and GenAI architectures. Set up and maintain CI/CD pipelines for application builds and platform deployments.

Follow established procedures for escalating unresolved issues with clear problem statements and diagnostics. Document technical knowledge, deployment steps, and operating procedures in reusable formats. Experience with Apache Beam and Java/Spring Boot is beneficial. This role also involves decision-making, process optimization, resource management, and team supervision to ensure task execution and meet organizational goals.

Possess strong hands-on experience in data engineering, cloud-native engineering, and delivering production-grade AI/ML solutions on Google Cloud. Exhibit practical experience with Vertex AI, including model development, integration, and deployment patterns for enterprise scenarios. Ability to build and optimize ETL/data pipelines using Python, Java, Go, or Scala, and execute workloads on Spark, Flink, and Google Cloud Dataflow is crucial.

Demonstrate working knowledge of GenAI integration patterns for automation, insight generation, predictive analytics, and intelligent data workflows. Understand token economics for GenAI solutions, covering prompt efficiency, consumption, latency, cost optimization, and governance. Awareness of multi-cloud deployment patterns and the ability to translate architectures between Google Cloud and Microsoft Azure is required.

Possess basic to working knowledge of relevant Microsoft Azure cloud services for data platforms, AI/ML workloads, and cloud-native deployment. Exhibit strong programming skills in Java, Python, Go, and scripting languages, with the ability to troubleshoot and optimize. A good understanding of application and container lifecycle management is essential. Experience setting up CI/CD pipelines for builds and deployments is expected.

A Professional or Practitioner-level Google Cloud certification with demonstrated hands-on public cloud delivery experience is required. Strong analytical thinking, problem-solving abilities, and a structured approach to escalation, documentation, and knowledge sharing are vital. Clear communication skills and the ability to collaborate effectively with product, data, platform, and engineering teams in a delivery-focused environment are necessary. Familiarity with Apache Beam and Java/Spring Boot is a plus.