Position Overview
Responsibilities - Architect, build, and deploy high-performance machine learning systems across the entire ML lifecycle.
- Scale data engineering and feature pipelines using SQL, Python, and distributed computing frameworks like Spark or Ray.
- Train, tune, and scale supervised learning models using gradient boosting (XGBoost, LightGBM) and Deep Learning architectures (PyTorch, TensorFlow).
- Write clean, object-oriented, and modular production code to transition models from research to high-performance serving environments.
- Design and maintain robust MLOps pipelines, including automated retraining, versioning, and CI/CD for machine learning.
- Monitor production models for data drift and performance degradation, implementing automated alerting and fallback mechanisms.
- Design rigorous A/B and multivariate tests to measure the business impact of ML models.
Requirements - 5–8+ years of experience as a Mac...