Google AI Lead Architect

We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. 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. Architect and Design: Lead the design and development of enterprise-grade AI applications and platforms, with a focus on scaling AI solutions for production. 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. The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.

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