Fractional AI Architect Consultant

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: