GCP Cloud Architect

Job Description

The role focuses on designing, migrating, and optimizing Google Cloud Platform (GCP) solutions to deliver secure, scalable, and high‑performance cloud infrastructures for the organization.

• Design and implement robust cloud architectures on Google Cloud Platform (GCP).
• Lead the cloud foundation build, ensuring best practices in security, scalability, and performance.
• Manage and execute the migration of on‑premises infrastructure to GCP.
• Implement and optimize Vertex AI solutions for advanced machine learning and AI capabilities.
• Collaborate with cross‑functional teams to understand business requirements and translate them into technical solutions.
• Provide technical leadership and mentorship to junior engineers and architects.
• Ensure compliance with industry standards and regulatory requirements.
• Troubleshoot and resolve complex technical issues related to GCP infrastructure and services.
• Stay updated with the latest GCP features, tools, and best practices.

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
• Minimum of 5 years of hands‑on experience with GCP architecture and services.
• Proven experience in cloud foundation build and on‑premises to GCP migration projects.
• Strong expertise in Vertex AI and implementing machine learning models on GCP.
• Proficiency in GCP services such as Compute Engine, Cloud Storage, Big Query, Cloud Functions, and Kubernetes Engine.
• Excellent understanding of networking, security, and IAM in GCP.
• Strong problem‑solving skills and ability to work in a fast‑paced environment.
• Excellent communication and collaboration skills.

• Experience with Terraform, Ansible, or other infrastructure as code (IaC) tools.
• Knowledge of Dev Ops practices and CI/CD pipelines.
• Proficiency in scripting languages such as Python, Bash, or Power Shell.
• Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
• Familiarity with monitoring and logging tools such as Stackdriver, Prometheus, and Grafana.
• Understanding of data engineering concepts and tools like Dataflow, Dataproc, and Pub/Sub.
• Experience with API management and microservices architecture.
• Knowledge of security best practices, including encryption, key management, and identity management.
• Familiarity with other cloud platforms like AWS or Azure.
• Experience with cloud‑native application development and serverless architectures.
• Proficiency in designing and implementing disaster recovery and business continuity plans.
• Knowledge of cloud cost management and optimization strategies.
• Experience with hybrid cloud environments and multi‑cloud strategies.
• Familiarity with cloud compliance frameworks such as GDPR, HIPAA, and SOC 2.
• Proficiency in using GCP's AI and machine learning tools, such as AutoML and AI Platform.
• Experience with data warehousing solutions like Big Query and data lake architectures.
• Knowledge of cloud networking concepts, including VPC, VPN, and interconnects.

• GCP Professional Cloud Architect certification.
• Experience with hybrid cloud environments and multi‑cloud strategies.
• Knowledge of machine learning frameworks such as Tensor Flow, PyTorch, or scikit‑learn.
• Experience with serverless computing and event‑driven architectures.

• Cloud Infrastructure Uptime:
  Measure the availability and reliability of the cloud infrastructure.
• Cost Optimization:
  Track cloud spending and identify opportunities for cost savings.
• Migration Success Rate:
  Evaluate the success of on‑premises to GCP migration projects.
• Performance Metrics:
  Monitor response times, latency, and throughput of cloud applications.
• Security Compliance:
  Ensure the environment adheres to security best practices and compliance requirements.
• Scalability and Flexibility:
  Measure the ability to scale resources based on demand.
• Incident Response Time:
  Track time taken to detect, respond to, and resolve incidents.
• User Satisfaction:
  Gather feedback from stakeholders and end‑users.
• Innovation and Improvement:
  Measure frequency and impact of new features and optimizations.
• Training and Development:
  Track ongoing…