Google AI Lead Architect

Work you’ll do

• Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini; optimize for scalability, reliability, security, and cost.
• Design, fine-tune, evaluate, and govern LLM solutions with Gemini on Vertex AI (prompt/tool/function calling, safety policies, Vector Search, evaluation); implement deployment, inference optimization, and monitoring.
• Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability.
• Define end-to-end architectures across data pipelines, feature engineering, 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 application and agentic design patterns.
• Implement security and governance for AI/ML systems (data privacy, model poisoning, adversarial attacks); apply Gemini safety features and enterprise guardrails.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering or a related technical field.
• 8+ years’ experience as a Software or Solution Architect, with a strong focus on application development and scaling solutions for production environments.
• 5+ years hands-on with Google Cloud, including 2+ end-to-end enterprise implementations in production.
• 4+ years designing and implementing Google Cloud networks, security controls, and landing zones using Terraform.
• 3+ years building and operating containerized workloads on GKE (autoscaling, ingress, monitoring/observability).
• 3+ years implementing CI/CD and DevSecOps with Cloud Build, GitHub Actions, or Jenkins.
• 3+ years executing migration or modernization programs to Google Cloud (rehost, replatform, refactor).
• 2+ years applying AI/GenAI on Google Cloud with Vertex AI and Gemini, including 1+ years’ production deployment (e.g. RAG with Vertex AI Search/Vector Search, prompt design, safety policies, observability).
• Deep understanding of AI/ML concepts, including experience with LLMs and their application in enterprise settings.
• Experience implementing multiple AI solutions in a professional, real-world environment.
• Strong understanding of security implications related to AI/ML systems (e.g., data privacy, model poisoning, adversarial attacks).
• Familiarity with various hyperscaler tools and services.
• Hyperscaler Architect certification is required (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert, or GCP Professional Cloud Architect).
• Ability to travel up to 50% based on the work you do and the clients and industries/sectors you serve.