GCP AI Platform Architect / Lead AI Platform Engineer

Requirements:

• GCP Expertise (verifiable - ask for production examples): GCP is their primary cloud not secondary experience alongside AWS/Azure. Production deployments across most of: Vertex AI, Cloud Run or GKE, Pub/Sub, BigQuery, Secret Manager, VPC Service Controls, IAM + Workload Identity. Has designed for GCP from scratch, not migrated from another cloud, end-to-end ownership
• AI / Backend Engineering: Python is the primary language - production-grade service/API development, not scripting or data science only. Strong track record building distributed systems and integrating LLMs.
• Agentic Architecture (must be production, not PoC): Hands-on production experience with at least one: LangGraph, Google ADK, CrewAI, or custom multi-agent orchestration layer. RAG pipelines shipped to production. Google ADK: candidate must be able to explain what it is, when to use it, and how it compares to LangGraph and custom orchestration. AI agent workflows, ReAct prompting, and Function Calling in production environments
• Multi-Tenant Architecture: Has designed a multi-tenant SaaS platform end-to-end - not just contributed. Can articulate tenant isolation strategies: IAM boundary design, data isolation per tenant, VPC controls.
• API Design & Integrations: Proven ability to create secure, high-performance APIs capable of asynchronously managing traffic and communication between multiple decoupled services.
• Enterprise Security: Practical knowledge of data isolation in multi-tenant SaaS architectures, IAM, and securing cloud-based environments.
• Vector Databases: Hands-on experience with semantic search and at least one of: Pinecone, Weaviate, pgvector, or Vertex Matching Engine.

Additional advantages:

• Experience in market research, qualitative NLP, or social listening platforms
• Familiarity with enterprise research tooling or data platforms
• Experience with streaming architectures and real-time conversational AI patterns