Key Responsibilities:
- Design, develop, train, test, and deploy end-to-end ML models for production-scale applications.
- Work across the entire ML lifecycle - from data ingestion and feature engineering to model training, evaluation, and deployment.
- Build and maintain MLOps pipelines for scalable and reliable model deployment in real-time environments.
- Collaborate with cross-functional teams (engineering, product, and data) to translate business problems into ML solutions.
- Evaluate and optimize model performance, ensuring efficiency, scalability, and robustness.
- Stay updated on the latest trends in AI/ML, model monitoring, and automation frameworks.
- Experience in building real-time inference pipelines and production-grade ML systems.
- Proficiency in Python and common ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Familiarity with cloud platforms (AWS, Azure, or GCP) and CI/CD for ML.
- Excellent analytical and problem-solving skills with attention to scalability and performance.
- Bachelor's or Master's degree from a Tier 1 institution
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