Key Responsibilities: Model Development & Optimization: Design, develop, and optimize machine learning models for real-world applications, ensuring high accuracy, scalability, and efficiency. ML Pipeline & Deployment: Build and maintain scalable ML pipelines using cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
Feature Engineering & Data Processing: Collaborate with data engineers to preprocess, clean, and transform large datasets for training and inference.
Productionization: Deploy ML models into production, monitor performance, and continuously improve them through A/B testing and retraining.
Collaboration: Work closely with cross-functional teams including software engineers, product managers, and business stakeholders to align ML solutions with business objectives.
MLOps & Automation: Implement MLOps best practices, automate model training and deployment, and e...