Lead Architect_AI

Responsibilities Define enterprise AI architecture, reference patterns, and roadmaps on GCP with multi-cloud portability. Design secure RAG and agentic AI platforms: orchestration layers, tool registries, vector stores, policy/guardrail services, and human-in-the-loop review. Select foundation models and fine-tuning strategies (prompt, LoRA, full/PEFT) and govern model lifecycle with registries and approvals. Establish platform capabilities for data governance, lineage, and observability (data/model/LLM evals). Guide cost, performance, and reliability trade-offs; build capacity plans and SLOs. Influence standards, conduct architecture reviews, and mentor teams across product lines. Desired Technical Skills Architecture: event-driven, microservices, streaming, and data mesh patterns. AI Platform: Vertex AI, Matching Engine, Vector DBs, LangChain/LlamaIndex, feature stores. Security & Governance: IAM, VPC-SC, DLP, KMS, PII redaction, safety filters, policy engines. MLOps/LLMOps: model registries, CI/CD, canary/shadow deployments, eval frameworks. Cloud: GCP primary; working knowledge of AWS and Azure AI services. Preferred Experience & Capabilities Led design of large-scale AI platforms including agent orchestration and tool-use patterns. Experience with data governance, compliance, and risk controls for AI systems. Executive communication, influence without authority, and mentorship across teams. Note: Primary development environment is GCP (Google Cloud Platform). Flexibility to work with AWS and Azure is desired for portability and interoperability.