Technologies and systems I leverage to design, deploy, and scale intelligent infrastructure.
Core Engineering
Python
Primary language for building production ML systems, APIs, and automation workflows.
Python
Primary language for building production ML systems, APIs, and automation workflows.
SQL
Used for querying, cleaning, and transforming structured data.
Git
Version control for tracking code changes and experiments.
Github
Hosting and documenting codebases and projects.
FastAPI
Designing high-performance REST APIs for serving ML models and RAG systems.
PyTorch
Developing deep learning, graph-based, and custom neural architectures.
Scikit-learn
Classical ML pipelines, feature engineering, and evaluation frameworks.
Pandas
Data wrangling, analysis, and preprocessing.
Numpy
Numerical computations and array operations.
OpenCV
Image preprocessing and computer vision tasks.
OpenAI APIs
Integrating generative models and LLM-based workflows.
Matplotlib / Seaborn
Visualizing data and model performance.
Plotly
Creating interactive data visualizations for the web.
Apache Spark
Distributed processing for large-scale datasets.
Google Dataproc
Manage Spark environment for cloud workflows.
AWS
Running compute instances, storing data, and experimenting with SageMaker.
Google Colab
Notebook-based model prototyping with GPU support.
Notion
Project planning, documentation, and knowledge management.
Obsidian
Personal knowledge base for technical notes and research.
Airflow
Scheduling and managing ETL pipelines and batch jobs.
Linux (WSL)
Local Linux environment for ML development on Windows.
Docker
Containerizing environments and ML pipelines.
Jupyter Notebooks
Interactive development and rapid experimentation.