Jean Almario
Computer Science student. Full-Stack & ML/AI Engineer.
Computer Science student at Texas Tech University (GPA 3.9/4.0, expected May 2027), focused on full-stack development and applied machine learning. I build production systems: RAG pipelines for enterprise document search and multi-agent AI platforms. Currently an AI/ML Fellow with Break Through Tech AI (Cornell Tech), building a harmful algal bloom forecasting model with Oregon State University's Socio-Environmental Analysis Lab.
I build systems I would actually use.
I like building things that solve a real problem for me or for people around me. Most of my projects started that way: a small shop that needed to sell online, engineers who could not find answers in their manuals, friends who do not speak the same language. I care that it works in production, not just in a demo.
class JeanAlmario: def __init__(self): self.role = "Full-Stack Developer & ML/AI Engineer" self.university = "Texas Tech University" self.gpa = 3.9 self.grad_year = 2027 def experience(self): return [ "Web Student Assistant @ TTU Rawls College (2026-present)", "Software Engineer Intern, ML @ Health Safety & Environment (2025)", ] def fellowships(self): return [ "AI/ML Fellow, Break Through Tech AI (Cornell Tech) (2026-present)", "Capstone Researcher, Oregon State University (2026-present)", ] def skills(self): return ["Python", "TypeScript", "React", "Next.js", "PyTorch", "scikit-learn", "Node.js", "AWS", "Docker", "Azure"]
Selected work
Multi-Agent AI Website Generation Platform
A multi-agent system that generates and deploys full-stack websites from natural-language prompts. Coordinated agents handle code generation, dependency installation, and cloud deployment, with a real-time dashboard tracking agent execution and deployment status. Featured by MLH.
RAG Documentation Assistant
Owned the evaluation and quality layer of a production RAG assistant so field engineers can query manuals and SOPs in natural language. Built a 30-question test set and RAGAS pipeline, then tested 12 chunking and retrieval configurations.
Full-Stack E-Commerce Platform
Production platform with REST APIs for products, users, carts, and orders, plus role-based access. Admin and customer features cover product management, search, and order tracking.
HAB Forecasting Model
Co-developing a classification model to forecast harmful algal bloom risk in coastal ecosystems. Processed 11,100+ oceanographic records into an ~8,000-sample training set and engineered 20+ predictive features with a leave-future-out cross-validation pipeline.
Experience & fellowships
Texas Tech University — Rawls College of Business
JAN 2026 – PRESENT- Improved the Rawls College website to 90% ADA accessibility compliance (HTML, CSS, JS, CMS)
- Standardized content workflows, improving site usability and update turnaround
- Worked with non-technical stakeholders to deploy and verify production changes
Health, Safety & Environment
JUN – AUG 2025- Owned the evaluation and quality layer of a production RAG system (LlamaIndex, Azure OpenAI, Azure AI Search)
- Built a 30-question test set and RAGAS pipeline to score faithfulness and context precision/recall
- Tested 12 chunking/retrieval configurations, raising context precision from 68% to 82% and faithfulness from 79% to 91%
Break Through Tech AI — Cornell Tech
MAY 2026 – PRESENT- Selected for a 1-year competitive fellowship pairing a Cornell-certified ML curriculum with an industry-partnered capstone
- Completed Machine Learning Foundations (Cornell Tech): ML lifecycle, supervised learning, neural networks, LLMs
Oregon State University — Socio-Environmental Analysis Lab
SEP 2026 – PRESENT- Co-developing a classification model to forecast harmful algal bloom risk in coastal ecosystems
- Processed 11,100+ oceanographic records into an ~8,000-sample training set from Copernicus Marine data
- Engineered 20+ predictive features; built a leave-future-out cross-validation pipeline