Principal AI Architect

Role Overview

We are seeking a hands-on AI Architect to design and implement intelligent enrollment systems that leverage large language models, distributed agents, and enterprise data platforms to improve student engagement and enrollment conversion.

This role will lead the architecture of a decision intelligence platform that analyzes student application data, generates AI-driven insights, and recommends next-best actions for enrollment specialists.

The platform integrates Google Cloud AI services, Salesforce data, and multi-agent orchestration frameworks to deliver scalable AI capabilities across the enrollment lifecycle.

Key Responsibilities

AI System Architecture

Design scalable AI architectures using Google Cloud services including:

• BigQuery
• Vertex AI - Gemini models
• Cloud Run
• Cloud Functions

Architect distributed AI systems capable of orchestrating multiple services and agents to process student data and generate insights.

LLM & Generative AI Systems

Design and implement LLM-powered workflows including:

• AI-generated enrollment insights
• application and transcript summarization
• next-best-action recommendations
• AI-assisted outreach messaging

Develop prompt engineering frameworks and structured output systems to ensure reliable AI responses.

Multi-Agent Orchestration

Design architectures that coordinate specialized AI agents responsible for:

• data retrieval
• student risk assessment
• AI insight generation
• recommendation systems

Implement orchestration patterns using containerized microservices and REST APIs.

Data & Analytics Integration

Leverage enterprise data sources including:

• Salesforce CRM
• BigQuery datasets
• enrollment activity logs
• application and transcript data

Design efficient data retrieval pipelines that feed AI services with relevant context.

Enterprise Integration

Integrate AI systems with enterprise applications including:

• Salesforce Lightning
• enrollment specialist dashboards
• internal analytics tools

Ensure AI services can be embedded within existing workflows.

Governance & Reliability

Ensure production-ready AI systems by implementing:

• monitoring and logging
• prompt reliability safeguards
• feedback loops for AI output improvement
• responsible AI practices

Required Qualifications

• 8+ years experience in cloud architecture, AI engineering, or distributed systems
• Strong experience with Google Cloud Platform
• Hands-on experience with:
• Vertex AI
• BigQuery
• Cloud Run
• Cloud Functions
• Experience designing LLM-powered systems
• Strong knowledge of Python and Node.js
• Experience integrating AI systems with enterprise platforms such as Salesforce

Preferred Qualifications

• Experience building multi-agent AI systems
• Experience implementing RAG or AI retrieval architectures
• Experience building AI systems for education, CRM, or customer lifecycle platforms
• Experience deploying AI solutions in regulated or enterprise environments