Fractional AI Architect (Consultant)

Generative AI, ML Systems & Scalable Platform Architecture

Contract / Fractional Engagement Remote

Overview

Bridge-it.ai An AI-driven SaaS platform operating in the career readiness and education technology space is seeking a Fractional AI Architect to conduct an architecture review and provide technical guidance for the platform’s AI and data systems.

Experience in the U.S. K-12 education ecosystem or EdTech platforms is highly desirable, particularly in systems that support students, educators, counselors, or workforce readiness initiatives.

The platform combines Generative AI copilots, retrieval-augmented generation (RAG), knowledge graphs, and traditional machine learning models to support career exploration, pathway planning, and personalized recommendations for students.

The engagement focuses on conducting a structured architecture audit and evaluating whether the current system design aligns with the platform’s long-term goals for scalability, reliability, observability, and continuous improvement.

The consultant will collaborate with engineering and product leadership to identify architectural gaps and provide recommendations for strengthening the AI platform.

This role is intended for senior AI architects or principal-level engineers who have previously designed and operated production AI systems at scale.

Scope of Engagement

The consultant will review the current system architecture and provide recommendations across several key areas.

AI Platform Architecture Review

Conduct a structured audit of the platform’s AI architecture, including:

• generative AI copilot design
  
  

• agentic workflow orchestration
  
  

• retrieval-augmented generation pipelines
  
  

• knowledge retrieval systems
  
  

• vector database usage
  
  

• knowledge graph integration
  
  

• context management and AI memory strategies
  
  

• prompt and instruction architecture
  
  

Assess whether the current design supports:

• reliable AI behavior
  
  

• scalable inference
  
  

• controllable AI workflows
  
  

• maintainable system architecture.
  
  

Generative AI & LLM Systems

Evaluate the architecture and technical strategy related to:

• LLM model selection
  
  

• API-based vs self-hosted model strategies
  
  

• embeddings and vector search pipelines
  
  

• prompt and context engineering
  
  

• RAG architecture
  
  

• agent orchestration frameworks
  
  

• guardrails and reliability mechanisms.
  
  

Provide recommendations to improve:

• model response quality
  
  

• latency
  
  

• cost efficiency
  
  

• system reliability.
  
  

Traditional Machine Learning Systems

Review architecture related to traditional ML use cases such as:

• recommendation systems
  
  

• predictive analytics
  
  

• forecasting models
  
  

• clustering and segmentation pipelines.
  
  

Assess the architecture supporting:

• training pipelines
  
  

• experimentation workflows
  
  

• model deployment
  
  

• model lifecycle management.
  
  

Copilot Interaction & Agentic Workflows

Evaluate the design of AI-driven workflows supporting the copilot experience, including:

• user-initiated interactions
  
  

• event-driven AI recommendations
  
  

• multi-step reasoning workflows
  
  

• recommendation pipelines.
  
  

Provide guidance on improving:

• intent detection
  
  

• workflow orchestration
  
  

• AI reasoning pipelines
  
  

• reliability and safety mechanisms.
  
  

Platform Architecture & System Design

Assess the platform’s core architecture, including:

• microservices architecture
  
  

• event-driven system design
  
  

• message-based communication patterns
  
  

• API architecture
  
  

• service boundaries and modularity.
  
  

Review the application of architectural patterns such as:

• event-driven architecture
  
  

• message-driven systems
  
  

• asynchronous processing
  
  

• hexagonal / ports-and-adapters architecture.
  
  

Provide recommendations for improving:

• scalability
  
  

• reliability
  
  

• maintainability
  
  

• operational efficiency.
  
  

Observability, Monitoring & Evaluation

Evaluate the platform’s ability to monitor both traditional services and AI systems.

Assess current capabilities in areas such as:

• distributed tracing
  
  

• system metrics and logging
  
  

• operational monitoring
  
  

• AI workflow traceability
  
  

• prompt and model evaluation
  
  

• experiment tracking.
  
  

Provide recommendations for implementing robust observability and evaluation frameworks.

Continuous Learning & Feedback Systems

Review architecture supporting long-term improvement of AI systems, including:

• user feedback capture
  
  

• interaction analytics
  
  

• model performance evaluation
  
  

• experimentation frameworks
  
  

• learning pipelines.
  
  

Provide recommendations for enabling continuous learning and system improvement.

Deliverables

The consultant will deliver:

• a structured architecture assessment report
  
  

• identified design gaps and architectural risks
  
  

• prioritized technical recommendations
  
  

• suggested architecture evolution roadmap.
  
  

The consultant will present findings to the leadership and engineering teams.

Required Experience

Candidates should have substantial experience designing AI-driven software systems in production environments.

Minimum qualifications include:

• 12+ years of experience building distributed software systems and AI/ML platforms, any less experience - no need to apply
  
  

• strong hands-on experience building Generative AI applications
  
  

• deep understanding of:
  
  

  • Retrieval-Augmented Generation (RAG)
    
    

  • prompt and context engineering
    
    

  • embedding pipelines
    
    

  • vector search systems
    
    

  • agentic AI architectures
    
    

• practical experience implementing traditional machine learning systems, including:
  
  

  • recommendation systems
    
    

  • forecasting models
    
    

  • predictive analytics pipelines.
    
    

Software Architecture Experience

Demonstrated experience designing modern distributed systems using:

• microservices architecture
  
  

• event-driven systems
  
  

• message-based system communication
  
  

• asynchronous processing patterns
  
  

• hexagonal architecture / ports-and-adapters.
  
  

Cloud & Infrastructure

Experience building and operating systems on modern cloud platforms such as:

• Google Cloud
  
  

• AWS
  
  

• Azure.
  
  

Experience with containerized systems and cloud-native infrastructure.

Observability & Production Systems

Strong experience operating production systems with:

• distributed tracing
  
  

• system monitoring and metrics
  
  

• centralized logging
  
  

• operational diagnostics.
  
  

Experience with AI system observability and evaluation tools is highly desirable.

Preferred Experience

• Experience building AI copilots or conversational AI systems
  
  

• Experience with agent orchestration frameworks
  
  

• Experience with vector databases and knowledge graphs
  
  

• Experience designing AI evaluation pipelines
  
  

• Prior experience in EdTech platforms
  
  

• Familiarity with U.S. K-12 education systems.
  
  

Engagement Model

• Fractional consulting engagement (part-time).
  
  

• Initial architecture review phase followed by optional advisory support.
  
  

• Expected duration for the initial engagement: 1–3 months.
  
  

Ideal Candidate Profile

This role is best suited for professionals who have previously served as:

• Principal Architect
  
  

• AI Platform Architect
  
  

• Staff / Principal Engineer
  
  

• ML Platform Architect
  
  

• AI Infrastructure Architect
  
  

and who have direct experience building and operating production AI systems.