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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 | SQLite | WebSockets | OpenAI API

An autonomous LLM assistant that reasons about calendar events, biometrics, and device state to execute multi-step routines without being explicitly asked. Orchestrates 10+ live integrations (Spotify, Google Calendar, Garmin, smart home) via a tool-calling agent loop. Trains local stress and sleep classifiers on wearable data using scikit-learn, with a confidence system that learns from user feedback and adapts in real time.

Drift (Sentiment Analysis & Trend Detection)

Python | FastAPI | React | DistilBERT | HuggingFace | SHAP | Recharts

Fine-tuned DistilBERT on 40K+ labeled Reddit and Twitter posts for 3-class sentiment classification, achieving 73.1% F1 macro on a 10,360-sample held-out test set. Built an analytics layer that computes rolling sentiment averages across configurable time windows, detects anomaly shifts with severity scoring, and surfaces TF-IDF keywords per class with SHAP token-level explanations.

Wavelength (Music Mood Recommendation Engine)

Python | FastAPI | React | XGBoost | PyTorch | SHAP | Recharts

Trained Random Forest, XGBoost, and a PyTorch MLP on 89K Spotify tracks across 7 mood classes. XGBoost reached 99.6% and MLP reached 93.5% F1 macro, illustrating how tree-based and neural models learn threshold boundaries differently. Engineered 13 features from raw audio data and used SHAP TreeExplainer to generate plain-English mood explanations, powering a recommendation engine with cosine similarity ranking and fine-tune sliders.

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

    Philosophy

    Family

    Food

    Gym

    Resume

    My Life in Data

    GitHub

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    Total Commits

    Projects

    3

    Completed Projects

    Technologies

    25+

    Tools & Languages

    Experience

    3+

    Years in Data Science

    Tech Stack Usage

    My Typing Rhythm