Smart IT Frame LLC
GCP Architect - Kubernetes strong & ML algorithms
Smart IT Frame LLC, Dallas, Texas, United States, 75201
Experience Level: 15+ yearsNote: Only locals in Texas or 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.
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.