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Google Cloud Architect

2 months ago

charlotte, United States Smart IT Frame LLC Full time

Experience Level: 15 Years

Note: Only locals in NC

Key Responsibilities:

  • Design and architect scalable cloud platforms to develop, deploy scalable integration and Peta Byte scale data migration, and AI solutions leveraging ML techniques.
  • Drive Joint Architecture Design to collaborating with business stakeholders, data scientists, engineering teams, product, and other key partners to gather functional, non-functional requirements for solving AI use case on the AI Platform
  • Design foundational solutions to enable multiple batch and real time integration patterns between On Premise and Cloud
  • Evaluate emerging technologies and tools in AI area and do fitment analysis to the Enterprise AI Platform and capabilities strategy.
  • Define and implement GCP architecture best practices, frameworks, and standards.
  • Lead GCP infrastructure setup, including cloud services selection, data pipelines, and model deployment.
  • Ensure robustness, reliability, and scalability of solutions in production environments.
  • Design and implement data governance, security, and compliance measures for data and non-data objects.
  • Optimize ML workflows for performance, cost efficiency, and resource utilization.
  • Provide technical leadership and mentorship to development teams.
  • Communicate architecture decisions and solution design to stakeholders and executives.


Key Requirements:

  • Proven experience as GCP Architect
  • Deep understanding of ML algorithms, Vertex ASI, Big Query, Data Proc, Composer, STS, DataPlex
  • Strong knowledge of development life cycle, software engineering principles, data engineering principles
  • Experience with containerization and orchestration tools on prem and cloud (e.g GKE, OpenShift Container Platform, Docker, Kubernetes) for deploying AI/ML models.
  • Ability to design and optimize distributed computing systems for AI/ML workloads.
  • Familiarity with DevOps practices, CI/CD pipelines, and automation tools in AI-ML contexts.
  • Excellent problem-solving skills and ability to address complex technical challenges.
  • Effective communication skills to collaborate with cross-functional teams and stakeholders.