GenAI Architect - 11096

What You'll Do:

• Enterprise GenAI Architecture: Design and define the architectural vision for multi-agent AI systems using Google ADK, Vertex AI Agent Builder, and GCP services, ensuring solutions scale across the enterprise and align with business objectives.
• Platform Strategy & Standards: Establish architectural standards, design patterns, and best practices for AI development on Google Cloud Platform, including model deployment strategies, agent orchestration patterns, and integration frameworks.
• Vertex AI Platform Design: Architect comprehensive AI platforms leveraging Vertex AI capabilities including model training pipelines, fine-tuning strategies, vector search architectures, model governance, and production monitoring frameworks.
• Cloud Integration Architecture: Design sophisticated integrations between AI agents and enterprise systems using Google Cloud services including BigQuery, Cloud Functions, Cloud Run, Pub/Sub, Dataflow, and third-party enterprise applications (Salesforce, SAP, etc.).
• Technical Leadership & Mentorship: Provide technical guidance and architectural oversight to AI developers, cloud engineers, and cross-functional teams. Review designs, conduct architecture reviews, and mentor team members on best practices.
• Responsible AI Framework: Define and implement enterprise-wide responsible AI frameworks using Vertex AI's governance tools, including bias detection, model explainability, compliance monitoring, privacy controls, and ethical AI guidelines.
• Scalability & Performance: Design for scale, reliability, and cost-optimization across AI workloads. Define strategies for model serving, caching, load balancing, and resource optimization on GCP infrastructure.
• Innovation & Evaluation: Research and evaluate emerging AI technologies, Google Cloud AI capabilities, and industry trends. Recommend adoption of new tools, frameworks, and architectural patterns to maintain competitive advantage.
• Stakeholder Engagement: Partner with executive leadership, business stakeholders, and technical teams to understand strategic priorities and translate them into actionable architectural roadmaps and technical strategies.

What You Will Bring to Coupa:

• Proven experience (10+ years) in software architecture, cloud architecture, or AI/ML architecture with at least 3-5 years focused on production AI systems.
• Demonstrated expertise designing and implementing enterprise-scale AI solutions on Google Cloud Platform with deep knowledge of Vertex GenAI Architecture and capabilities.
• Strong architectural knowledge of AI/ML systems, agentic AI patterns (ReAct, planning, multi-agent orchestration, tool use), and experience architecting solutions with Google ADK or Vertex AI Agent Builder.
• Expert-level understanding of Google Cloud Platform architecture including Vertex AI, BigQuery, Cloud Functions, Cloud Run, GKE, Pub/Sub, and enterprise integration patterns.
• Deep knowledge of MLOps architecture, including Vertex AI Pipelines, model versioning, A/B testing strategies, model monitoring, and CI/CD for ML systems.
• Proven track record of architecting and delivering multiple AI/ML solutions into production that created significant business impact.
• Strong proficiency in Python and architectural understanding of AI/ML frameworks including Google ADK, Vertex AI SDK, TensorFlow/JAX, and agent orchestration frameworks.
• Exceptional ability to communicate complex architectural concepts to executive leadership, technical teams, and non-technical stakeholders through documentation, diagrams, and presentations.
• Bachelor's degree in Computer Science, Data Science, or a related field; Master's degree or equivalent advanced experience preferred.


Preferred :
• Experience architecting solutions with Vertex AI advanced features including vector search (Matching Engine), custom training at scale, distributed training, and multi-modal AI systems.
• Deep expertise with Gemini API, embeddings architectures, and retrieval-augmented generation (RAG) patterns including hybrid search, reranking, and grounding strategies.
• Experience with enterprise architecture frameworks (TOGAF, Zachman) and cloud architecture certifications (Google Cloud Professional Cloud Architect, Professional ML Engineer).
• Hands-on experience architecting security and compliance frameworks for AI systems, including Google Cloud security best practices, IAM, VPC Service Controls, and data governance.
• Proven experience implementing responsible AI governance frameworks using Vertex AI Explainable AI, Model Monitoring, fairness metrics, and bias mitigation strategies at scale.
• Experience architecting real-time AI systems with low-latency requirements, streaming architectures, and event-driven patterns.
• Background in enterprise data architecture, data mesh patterns, or knowledge graph architectures integrated with AI systems.
• Published thought leadership, speaking engagements, or contributions to the AI/ML architecture community.