AI/ML Fellowship Program

Role Summary The AI/ML Fellowship Associate will participate in a structured, project-based learning program designed to build foundational and intermediate skills in artificial intelligence, machine learning, and data science. Fellows will work on real-world AI/ML projects, receive mentorship from industry experts, and collaborate with technical and business teams to gain practical experience across the complete machine learning lifecycle — problem framing, data engineering, model development, model evaluation, deployment, and monitoring. Throughout the fellowship, associates will apply statistical methods, machine learning algorithms, and AI frameworks to build innovative, production-ready models that solve real business and societal problems. Key Responsibilities • Project Execution & Documentation • Complete assigned AI/ML projects such as exploratory data analysis, feature engineering, model building, and hyperparameter tuning. • Build supervised and unsupervised machine learning models tailored to project requirements (e.g., regression, classification, clustering, recommendation systems). • Use Python libraries and ML frameworks such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch to implement models. • Document datasets used, feature engineering choices, model performance, and deployment workflows. • Research & Framework Development • Research AI/ML best practices, MLOps methodologies, and current state-of-the-art algorithms. • Propose enhancements to models, feature engineering approaches, and ML workflows. • Contribute to the creation of reusable model templates, experimentation logs, and evaluation frameworks. • AI/ML Deployment & Model Monitoring • Deploy machine learning models using APIs, cloud services, or containerized environments. • Support model monitoring and performance tracking to detect drift, fairness issues, or anomalies. Accelerated pathway to roles such as ML Engineer, Data Scientist, AI Research, ML OPs & AI Engineers