Responsibilities
Model Development and Deployment: Design, implement, and deploy machine learning models to solve complex problems across various domains, ensuring models are scalable, efficient, and production-ready.
Data Collection and Preprocessing: Collaborate with data scientists and engineers to collect, clean, and preprocess large datasets, ensuring data quality and suitability for model training.
Algorithm Optimization: Continuously optimize machine learning algorithms for performance, accuracy, and efficiency. Leverage techniques like hyperparameter tuning, feature engineering, and model ensemble methods.
Research and Innovation: Stay up-to-date with the latest developments in machine learning and AI. Evaluate and implement state-of-the-art techniques, frameworks, and libraries.
Model Evaluation: Develop and implement metrics and validation methods to evaluate the performance of machine learning models. Conduct regular model audits and retraining to maintain accuracy and relevance.
Infrastructure and Tools: Build and maintain the infrastructure for machine learning pipelines, including model training, testing, and deployment. Use cloud-based platforms (e.g., AWS, GCP) and frameworks like TensorFlow, PyTorch, or Scikit-learn.
Collaborative Problem Solving: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to integrate machine learning solutions into broader systems and applications.