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.

Responsibilities

• Architect and Design: Lead the design and development of enterprise-grade AI applications and platforms, with a focus on scaling AI solutions for production. This includes defining the technical architecture, selecting appropriate technologies, and ensuring solutions are robust, scalable, and secure.
• LLM and AI Integration: Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an emphasis on production-level performance and reliability.
• Enterprise Architecture: Collaborate with enterprise architects to ensure AI solutions align with the broader company's technical strategy, governance, and standards.
• Cloud and GenAI Native Development: Design and deploy applications using Cloud Native principles on a hyperscaler platform (AWS, Azure, GCP). Leverage a wide range of hyperscaler tools and services, including containers (Docker, Kubernetes), serverless functions, and managed databases.

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.