Solution Architect - GCP

Job Summary:

Solution Architect with deep proficient knowledge in Google Cloud Platform (GCP) and AI services to lead the design and deployment of intelligent, scalable, and secure cloud solutions. You'll play a pivotal role in shaping our cloud and AI strategy, bridging business needs with technical execution and driving innovation across the organization.

Key Responsibilities:

• Architect and implement end-to-end solutions using GCP services, with a focus on AI and machine learning capabilities.
• Collaborate with stakeholders to gather requirements and translate them into scalable, intelligent architectures.
• Lead cloud migration and modernization initiatives, integrating AI-driven components where applicable.
• Design and deploy models using GCP AI tools such as Vertex AI, BigQuery & Custom model development.
• Define best practices for cloud security, scalability, and cost optimization.
• Create architectural diagrams, documentation, and technical roadmaps.
• Provide technical leadership and mentorship to development and data science teams.
• Conduct architecture reviews and ensure alignment with enterprise standards.
• Stay current with GCP updates, AI trends, and emerging technologies.

Required Skills & Qualifications:

• Bachelor's or master's degree in computer science, Engineering, or related field.
• 7+ years of experience in software development and architecture roles.
• 3+ years of hands-on experience with GCP services (Compute Engine, Cloud Functions, BigQuery, Cloud Storage, etc.).
• Proven experience with GCP AI/ML services (Vertex AI, AutoML, BigQuery ML, etc.).
• Strong understanding of cloud-native design, microservices, and container orchestration (Kubernetes, GKE).
• Experience with CI/CD pipelines and DevOps practices.
• Excellent communication and stakeholder engagement skills.
• Ability to balance technical depth with business strategy.

Preferred Qualifications:

• GCP Professional Cloud Architect or Professional Machine Learning Engineer certification.
• Experience with hybrid and multi-cloud environments.
• Familiarity with infrastructure-as-code tools (Terraform, Deployment Manager).
• Background in data architecture, model lifecycle management, and MLOps.