Solution Architect

As a Cloud Solution Architect specialized in AI/ML and Google Cloud Platform (GCP), you will be responsible for designing and deploying intelligent, scalable, and secure cloud solutions. You will play a crucial role in shaping the cloud and AI strategy, aligning business requirements with technical implementation, and fostering innovation within the organization. Key Responsibilities: - Architect and implement end-to-end solutions using GCP services, focusing on AI and machine learning capabilities. - Collaborate with stakeholders to gather requirements and translate them into scalable, intelligent architectures. - Lead cloud migration and modernization projects, incorporating AI-driven components as needed. - Design and deploy models utilizing GCP AI tools like Vertex AI, BigQuery, and custom model development. - Establish best practices for cloud security, scalability, and cost optimization. - Develop architectural diagrams, documentation, and technical roadmaps. - Provide technical leadership and mentorship to development and data science teams. - Conduct architecture reviews to ensure compliance with enterprise standards. Required Skills & Qualifications: - Bachelor's or master's degree in computer science, engineering, or a related field. - Minimum of 5 years of experience in software development and architecture roles. - At least 3 years of hands-on experience with GCP services such as Compute Engine, Cloud Functions, BigQuery, and Cloud Storage. - Demonstrated expertise in GCP AI/ML services like Vertex AI, AutoML, and BigQuery ML. - Strong grasp of cloud-native design, microservices, and container orchestration (e.g., Kubernetes, GKE). - Proficiency in CI/CD pipelines and DevOps practices. - Excellent communication skills and ability to engage stakeholders effectively. - Capability to balance technical depth with business strategy. - GCP Professional Cloud Architect or Professional Machine Learning Engineer certification. Preferred Qualifications (if applicable): - Experience working with hybrid and multi-cloud environments. - Familiarity with infrastructure-as-code tools such as Terraform and Deployment Manager. - Background in data architecture, model lifecycle management, and MLOps.,