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About

Headshot of Ali Abouelazm

ML engineer building real-time AI systems: agentic LLM assistants, biosensor prediction pipelines, and computer vision tools that ship to users.

Combine software engineering, machine learning, and applied AI to move from raw data to production-ready models, across PyTorch pipelines, LLM tool-calling architectures, and full-stack AI applications.

Currently doing ML research at Texas A&M AgriLife, building predictive models on 50GB+ of livestock biosensor data and architecting scalable AWS pipelines for field researchers.

Incoming summer 2026 at Cloudflare on their AEO (AI Engine Optimization) team, working at the intersection of LLM behavior and content discoverability.

  • Based in Sugar Land, TX
  • Seeking full-time ML/AI Engineer roles · Available May 2027

Projects

Sonus: Agentic Smart Assistant

Python | FastAPI | React | scikit-learn | WebSockets | OpenAI/Anthropic APIs | SQLite

An autonomous LLM-powered assistant that reasons about your environment (calendar, biometrics, devices, and habits) to execute multi-step routines without being explicitly asked. Orchestrates 10+ real integrations (Spotify, Google Calendar, Garmin, smart home devices) via a tool-calling agent loop. Trains local stress and sleep predictors on wearable data using scikit-learn, with a confidence system that learns from feedback and adapts over time. Built with a FastAPI backend, React frontend, and real-time WebSocket layer.

clinix.ai

Python | FastAPI | Streamlit | OpenAI/Anthropic | scikit-learn | SQLAlchemy | SQLite

A medical triage system that uses LLM semantic parsing to convert free-text symptom descriptions into structured clinical features, then applies an ML classifier to produce risk scores and triage decisions. Achieves 0.92 F1-score on patient risk classification. Built with a FastAPI backend, SQLAlchemy-managed SQLite store, and a live Streamlit dashboard.

Causal Marketing Impact

Python | DoubleML | scikit-learn | Streamlit | Docker | GitHub Actions

A causal inference application using Double Machine Learning (DoubleML) to isolate the true return on marketing spend by controlling for confounding variables to separate real causal lift from naive correlation. Containerized with Docker and deployed via GitHub Actions CI/CD to an interactive Streamlit dashboard.

Experience & Leadership

    Skills/Interests

    Languages

    Python

    R

    SQL

    Java

    JavaScript

    TypeScript

    C/C++

    HTML/CSS

    AI/ML

    scikit-learn

    XGBoost

    CatBoost

    LightGBM

    TensorFlow

    PyTorch

    Keras

    Transformers

    Data/Viz

    pandas

    NumPy

    SciPy

    Dask

    GeoPandas

    Statsmodels

    Matplotlib

    Seaborn

    Plotly

    Tableau

    Interests

    Traveling

    Soccer

    Swimming

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    Gym

    Resume

    My Life in Data

    GitHub

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    Total Commits

    Projects

    8

    Completed Projects

    Technologies

    25+

    Tools & Languages

    Experience

    2+

    Years in Data Science

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