Google AI Architect

Experteer Overview As a Google AI Architect in Deloitte’s AI & Engineering team, you will design and deliver enterprise AI platforms on Google Cloud, accelerating client transformation. You will work with cross-functional teams to scale AI solutions, ensure security and governance, and drive modernization of data and technology platforms. You’ll shape architecture, deploy GenAI-powered applications, and implement scalable MLOps practices. This role offers impact across clients, from improving operations to enabling new digital ventures. Compensation / Benefits * Architect and deliver enterprise AI platforms on Google Cloud using Vertex AI and Gemini with a focus on scalability, reliability, security, and cost. * Design and govern LLM solutions, deployment, inference optimization, and monitoring for production readiness. * Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery Vector; manage context, retrieval, and observability. * Define end-to-end architectures across data pipelines, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps with Vertex AI Pipelines and Cloud Build. * Lead cloud-native development on GKE, Cloud Run, Pub/Sub, BigQuery, Cloud SQL/Spanner, Memorystore, and Terraform; enforce design patterns. * Implement security and governance for AI/ML systems, including data privacy, model poisoning, and adversarial defense; apply Gemini safety features. Tasks * Bachelor’s degree in Computer Science, Engineering or a related technical field * 6+ years as a Software or Solution Architect with production-scale application development * 5+ years hands-on Google Cloud experience with 2+ end-to-end enterprise deployments * 4+ years Terraform for Google Cloud networks, security controls, landing zones * 2+ years operating containerized workloads on GKE (autoscaling, ingress, observability) * 2+ years CI/CD and DevSecOps with Cloud Build, GitHub Actions, or Jenkins * 3+ years executing migration or modernization programs to Google Cloud * 2+ years applying AI/GenAI on Google Cloud with Vertex AI and Gemini, including 1+ year production deployment * Deep understanding of AI/ML concepts, LLMs in enterprise settings * Security considerations for AI/ML systems (data privacy, adversarial threats) * Familiarity with hyperscaler tools; Hyperscaler Architect certification required Key requirements *

Cloud data pipelines, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps with Vertex AI Pipelines and Cloud Build. * Lead cloud-native development on GKE, Cloud Run, Pub/Sub, BigQuery, Cloud SQL/Spanner, Memorystore, and Terraform; enforce design patterns. * Implement security and governance for AI/ML systems, including data privacy, model poisoning, and adversarial defense; apply Gemini safety features. Tasks * Bachelor’s degree in Computer Science, Engineering or a related technical field * 6+ years as a Software or Solution Architect with production-scale application development * 5+ years hands-on Google Cloud experience with 2+ end-to-end enterprise deployments * 4+ years Terraform for Google Cloud networks, security controls, landing zones * 2+ years operating containerized workloads on GKE (autoscaling, ingress, observability) * 2+ years CI/CD and DevSecOps with Cloud Build, GitHub Actions, or Jenkins * 3+ years executing migration or modernization programs to Google Cloud

• 2+ years applying AI/GenAI on Google Cloud with Vertex AI and Gemini, including 1+ year production deployment * Deep understanding of AI/ML concepts, LLMs in enterprise settings * Security considerations for AI/ML systems (data privacy, adversarial threats) * Familiarity with hyperscaler tools; Hyperscaler Architect certification required Key requirements *