Monticello Academy is seeking a contract-based Fractional CTO to guide the technical architecture and early development of an innovative education technology system. This role will support the creation of a scalable platform that transforms handwritten student work into actionable learning insights using OCR, computer vision, AI/LLMs, and modern data infrastructure.
This posting is part of a Request for Statements of Qualifications (RFSQ) process.
Respondents should review the full RFSQ for submission requirements, evaluation criteria, and scope expectations, which can be found here: https://utah.bonfirehub.com/opportunities/213553
About the Project
Monticello Academy is developing a system that allows teachers to maintain the cognitive and attentional benefits of paper-based learning while gaining the analytic power of digital environments. Student work will be scanned, parsed through OCR and AI models, tagged with structured metadata, and stored in a relational data backbone for longitudinal and cross-class analysis.
The platform will enable teachers to ask natural-language questions—such as “How have my students’ writing conventions improved this quarter?” or “Where is my 2nd period struggling with equivalent fractions?”—and receive structured insights drawn directly from authentic student work.
Role Overview
The Fractional CTO will provide technical leadership, architecture design, and engineering oversight for this early-stage project, ensuring that the system is robust, scalable, secure, and aligned with best practices in modern AI/ML and data engineering.
This is a contract role; level of effort and duration will be addressed through the RFSQ process.
Key Responsibilities
- Architecture & Systems Design
- Design an end-to-end pipeline for scan ingestion, OCR, LLM-based tagging and summarization, data storage, and querying.
- Recommend architectural approaches (e.g., Supabase/Postgres, AWS + S3, serverless functions, vector search).
- Ensure system modularity so components (OCR engine, AI model, data layer) can evolve or be swapped.
- AI / Data Pipeline Leadership
- Define data schemas for structured attributes, AI-generated summaries, and analytic tags.
- Establish reliable workflows for OCR processing, prompt engineering, tagging, and validation.
- Explore semantic search and NL→SQL capabilities.
- Security, Privacy & Compliance
- Ensure FERPA-compliant data handling, secure storage, role-based access controls, and audit logging.
- Recommend governance practices for versioning, human review, and AI confidence thresholds.
- Technical Roadmapping
- Produce technical roadmaps, development plans, and build-versus-buy evaluations.
- Assess staffing needs (fractional or contracted engineering resources) as the project grows.
- Collaboration & Communication
- Work closely with Monticello leadership, educators, and evaluation committees.
- Translate complex technical concepts into clear recommendations for non-technical stakeholders.
Preferred Qualifications
- Demonstrated experience designing and implementing AI-powered data systems, including OCR, NLP/LLMs, or ML-powered tagging workflows.
- Strong background with PostgreSQL, relational modeling, JSONB, and vector search (pgvector).
- Experience with serverless ingestion pipelines (AWS Lambda, Supabase Edge Functions, GCP Cloud Functions, etc.).
- Familiarity with FERPA or similar data privacy frameworks.
- Experience in education technology, assessment systems, multimodal AI, or analytics platforms is highly desirable.
- Ability to work in a fractional, consultative, or contract capacity and produce high-quality architectural deliverables.
Contract Structure
This posting does not constitute an offer of employment.
All interested individuals and firms must submit a Statement of Qualifications (SOQ) pursuant to Monticello Academy’s RFSQ.
The final award decision will be based on evaluation criteria outlined in the solicitation.
How to Respond
Submit your Statement of Qualifications as directed in the RFSQ: https://utah.bonfirehub.com/opportunities/213553. Questions and clarifications should be submitted through the mechanism specified in the RFSQ, not via LinkedIn.
