AI Infrastructure ArchitectProject Role AI Infrastructure Architect
Project Role Description Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must Have Skills
- Agent Development
Good to Have Skills
- NA
Experience
- Minimum 12 year(s) of experience is required
Educational Qualification
- 15 years full time education
Lead GCP Agentic AI EngineerLevel
- Principal or Staff
Experience
- 8 or more Years
Work Mode
- Remote or Hybrid
Employment
- Full Time
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
- Execution pipelines
- Evaluate and drive adoption of emerging GCP AI capabilities including:
- Gemini
- Grounding
- Agent to Agent protocols
- Open source frameworks
- Establish engineering standards design patterns and governance frameworks for responsible AI deployment covering:
- Safety
- Bias
- Auditability
- Lead cross functional technical initiatives spanning:
- Platform engineering
- Data teams
- ML research
- Product teams
- Partner with executive stakeholders to translate AI strategy into engineering execution.
- Provide technical due diligence for:
- Key vendor decisions
- Build or buy decisions
- Mentor and grow a team of senior and mid level engineers.
Required Skills and Qualifications
- 8 or more years of engineering experience.
- 3 or more years leading complex AI ML or agentic platform initiatives.
- Expert level GCP knowledge across the full stack including:
- Vertex AI
- GKE Autopilot
- AlloyDB
- Dataplex
- Apigee
- Emerging GCP AI infrastructure
- Deep mastery of agentic system design including:
- Hierarchical agent orchestration
- Dynamic tool calling
- Persistent memory
- Multi modal reasoning
- Human in the loop governance
- Robust expertise in:
- LLM fine tuning
- Model evaluation at scale using Vertex AI Pipelines
- Demonstrated ability to design for enterprise requirements including:
- Multi tenancy
- Data residency
- Zero trust security
- Regulatory compliance
- Experience with cost optimization at scale including:
- Spot and preemptible GPU workloads
- Resource quotas
- FinOps on GCP
- Exceptional communication skills with the ability to translate deep technical complexity for: