DE-Cloud Native AI and Data Engineer-GCP-GDSN02
Ernst and Young8 days ago
Bengaluru, IndiaSenior
Responsibilities
- Design, build, and optimize scalable data pipelines and ETL workflows using Python, Java, Go, or Scala on Apache Flink, Apache Spark, and Google Cloud Dataflow.
- Build and deploy cloud-native AI/ML solutions using Vertex AI for industry-specific use cases.
- Integrate GenAI capabilities into data pipelines and enterprise workflows for automation, insight generation, predictive analytics, and intelligent data processing.
- Apply token economics practices including prompt optimization, token monitoring, cost control, latency management, and performance trade-off assessment.
- Design reusable data engineering, AI/ML, and GenAI solution patterns for Google Cloud and adapt them for Microsoft Azure deployments.
- Develop application components and automation scripts, and manage build, packaging, deployment, monitoring, troubleshooting, and support across the application and container lifecycle.
- Set up and maintain CI/CD pipelines for application builds and target-platform deployments.
- Escalate unresolved issues, document technical knowledge and operating procedures, and create reusable runbooks and manuals.
- Allocate personnel, supervise team members, assign tasks, and support performance and process optimization as needed.
Requirements
- Strong hands-on experience in data engineering, cloud-native engineering, and production-grade AI/ML delivery on Google Cloud.
- Practical Vertex AI experience covering model development or integration, deployment patterns, and enterprise use cases.
- Ability to build and optimize pipelines using Python, Java, Go, or Scala and execute workloads on Spark, Flink, and Google Cloud Dataflow.
- Working knowledge of GenAI integration patterns and token economics, including prompt efficiency, usage monitoring, latency, cost optimization, and governance.
- Working knowledge of Microsoft Azure services and multi-cloud deployment patterns for data, AI/ML, and GenAI architectures.
- Strong programming and troubleshooting skills in Java, Python, Go, and scripting languages.
- Understanding of application and container lifecycle management, operational readiness, and production support.
- Experience setting up CI/CD pipelines for build, validation, and deployment.
- Professional or Practitioner-level Google Cloud certification and hands-on public cloud delivery experience.
- Strong analytical, problem-solving, communication, collaboration, documentation, and escalation skills.
- Preferred: Google Cloud Professional Cloud Architect or Professional Data Engineer certification.
- Preferred: Microsoft Certified Azure Solutions Architect Expert certification (AZ-305).
- Preferred: experience with GitHub Actions, Jenkins, or Azure DevOps.
- Preferred: familiarity with Apache Beam, Java/Spring Boot, and AI-assisted development tools such as GitHub Copilot.
Benefits
- Flexible work environment with globally connected teams.
- Health and wellness packages, rewards, and learning opportunities.
- Professional development through skill building, mentorship, and opportunities to take on leadership roles.
- Inclusive culture committed to diversity, equity, inclusion, and equal employment opportunity.
Tech Stack
Apache BeamApache FlinkApache SparkAzureGitHub ActionsGoGoogle CloudGoogle Cloud PlatformJavaJenkinsPythonScalaSpring Boot
Categories
Data EngineeringML Engineering
About Ernst and Young
Ernst & Young (EY) provides audit/assurance, tax, consulting, strategy and transactions services to enterprises, financial institutions, and public‑sector clients. Structured as a global network of partner‑owned member firms, it sells professional services on a fee basis, including a dedicated Financial Services Organization for banking, insurance, and capital markets. Headquartered in London, EY was formed in 1989 from the merger of Ernst & Whinney and Arthur Young, and operates in 150+ countries.