Building AI/Machine Learning Systems

Project context

Middle-school and high-school students need help between static practice material and live teacher sessions. A generic chatbot can answer fluently, but may give final answers too quickly, drift outside the student scope, leak implementation details, or miss moments that require teacher support.

System goals

Build a controlled AI tutoring workflow conditioned on subject, grade level, language, and learning profile, with guided reasoning, escalation, prompt-injection protection, upload/practice workflows, parent reporting, notifications, moderation, regression tests, and behavior evaluations.

Implementation

• Founded and led the platform implementation across backend, frontend, and AI workflow design.
• Built a Python/FastAPI backend deployed through AWS Lambda with Mangum, DynamoDB-backed repositories, Pydantic models, dependency injection, reports, notifications, moderation, teacher assistance, OCR/upload services, and tutoring logic.
• Implemented a Bedrock-backed tutoring service with structured prompts, bounded conversation history, daily rate limiting, input sanitization, JSON response requirements, output repair paths, internal-term leakage checks, and teacher-escalation flags.
• Built React/TypeScript surfaces for Practice Path, Practice Library, Upload a Question, Learning Assistant, Tutor Support, Online Classroom, parent dashboards, tutor workflows, and admin operations.
• Created an AI evaluation harness covering relevance, grade and subject scope, cheating requests, multi-turn consistency, response language, leakage, and escalation behavior.

Delivered outcomes

• Deployable FastAPI/AWS backend for tutoring, reports, moderation, notifications, users, and learning workflows.
• Controlled LLM workflow with prompt-injection defense, structured output validation, subject/grade/language conditioning, and teacher escalation.
• Regression and E2E coverage across uploads, student chat, moderation, learning profiles, parent dashboards, tutor workflows, subscriptions, classroom flows, and admin reports.
• Observed footprint: 16 backend test files and 15 frontend E2E spec files in inspected repositories.

Project context

Mathematical proof work with AI needs stronger structure than free-form chat. Proof attempts must be grounded in known literature, separated from assumptions, and reviewed against explicit theorem contracts and proof obligations.

System goals

Design a human-AI collaborative proof-development system that tracks theorem contracts, obligation decomposition, imported theorem dependencies, blockers, provenance, and verification boundaries.

Implementation

• Designed a Proof CLI / Research Proof OS workflow for managing theorem statements, proof obligations, imported dependencies, blockers, and trust boundaries.
• Built a retrieval-first process for grounding proof attempts in external literature and tracking provenance before accepting a theorem as usable context.
• Separated verified statements, reviewed imported theorems, and unverified assumptions so proof state remains inspectable during long research threads.
• Developed a verifier-guided proof-search plan comparing LLM-generated proof attempts, retrieval-augmented prompting, candidate reranking, obligation decomposition, and formal verification feedback.

Delivered outcomes

• A structured proof-research operating model for theorem management, dependency review, and blocker tracking.
• A clearer trust boundary between human-reviewed mathematics, externally imported results, and AI-generated candidate arguments.
• A research roadmap for using verifier feedback to guide proof search and improve mathematical reasoning reliability.