AI Infrastructure Architect

Project Role

Project Qualifications

• At least 12 years of experience is required.
• Fifteen years of full-time education is required.
• Agent development is listed as a must-have skill.

Embedded Role Scope

Key Responsibilities

• Define and own the long-term technical roadmap for GCP-based agentic AI systems across the organization.
• Design enterprise-scale, multi-tenant, multi-agent architectures supporting complex reasoning, planning, and execution pipelines.
• Evaluate and drive adoption of Gemini, grounding, agent-to-agent protocols, and open-source frameworks.
• Establish engineering standards, design patterns, and governance for responsible AI deployment, including safety, bias, and auditability.
• Lead cross-functional initiatives across platform engineering, data teams, ML research, and product teams.
• Partner with executive stakeholders to translate AI strategy into engineering execution.
• Provide technical due diligence for vendor and build-versus-buy decisions.
• Mentor and grow senior and mid-level engineers.

Required Skills and Qualifications

• At least 8 years of engineering experience.
• At least 3 years leading complex AI, ML, or agentic platform initiatives.
• Expert-level GCP knowledge across Vertex AI, GKE Autopilot, AlloyDB, Dataplex, Apigee, and emerging GCP AI infrastructure.
• Deep mastery of hierarchical agent orchestration, dynamic tool calling, persistent memory, multimodal reasoning, and human-in-the-loop governance.
• Strong expertise in LLM fine-tuning and model evaluation at scale using Vertex AI Pipelines.
• Experience with multi-tenancy, data residency, zero-trust security, and regulatory compliance.
• Experience with spot and preemptible GPU workloads, resource quotas, and FinOps on GCP.
• Exceptional communication skills for executive, customer, and engineering audiences.
• A proven track record of platform-level impact and driving organizational engineering culture.

Work Mode

• The embedded role states remote or hybrid work.

Employment