Technical Skills & Toolkit

An overview of technical competencies, frameworks, infrastructure tooling, and algorithms developed across academic research and applied machine learning engineering.

Core Competency Matrix

Deep Learning & Computer Vision

Architecting transformer-based modality encoders, vision backbones, and multimodal fusion mechanisms.

PyTorch Hugging Face Transformers Vision Transformers (ViT) Swin Transformer BERT / DeBERTa / RoBERTa CLIP Cross-Modal Attention TensorFlow / Keras OpenCV

MLOps, Infrastructure & Cloud

Production deployment, dataset versioning, artifact logging, and containerized inference microservices.

DVC (Data Version Control) MLflow Docker FastAPI GitHub Actions CI/CD AWS EC2 / S3 Cloudflare Workers Streamlit

Data Science & Classical Machine Learning

Statistical modeling, tabular gradient boosting, feature engineering, and model interpretability.

Scikit-learn XGBoost CatBoost SHAP (Explainable AI) Pandas NumPy Statistical Hypothesis Testing

Languages & Core Computer Science

Algorithmic problem solving, distributed systems fundamentals, database design, and systems engineering.

Python C++ C SQL (PostgreSQL, MySQL) JavaScript (ES6+) HTML5 / CSS3 DSA (250+ Solved) GATE CS Qualified (Score 514)

Engineering Principles

Reproducibility First: Deterministic seed control, immutable DVC pipeline dependencies, and version-pinned containerization.
Low-Latency Deployment: Asynchronous FastAPI endpoints with optimized ONNX/TorchScript graphs and memory-conscious inference engines.
Continuous Experiment Tracking: Granular artifact and metric logging with MLflow to ensure verifiable and audited model iterations.