AI/ML Fellowship Program (Mumbai)

As an AI/ML Fellowship Associate at our organization, you will be part of a structured, project-based learning program aimed at developing foundational and intermediate skills in artificial intelligence, machine learning, and data science. Throughout the 6-8 month full-time fellowship, you will have the opportunity to 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. Key Responsibilities: - Project Execution & Documentation - Complete assigned AI/ML projects including exploratory data analysis, feature engineering, model building, and hyperparameter tuning. - Develop supervised and unsupervised machine learning models tailored to project requirements using Python libraries and ML frameworks such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. - 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. - Integrate automation workflows and CI/CD principles into ML deployments using MLOps toolchains. - Stakeholder Collaboration - Work closely with mentors, technical team members, and domain experts to understand problem statements and validate outcomes. - Participate in peer code reviews, ML research discussions, project showcases, and technical presentations. - Incorporate feedback to improve model interpretability, decision transparency, and business relevance. Qualifications: - Graduate degree in AI, ML, Data Science, Computer Science, Statistics, Engineering, Mathematics, or related fields. - Recent graduates or early-career professionals with a strong passion for AI/ML preferred. - Proficiency in Python and familiarity with ML libraries (Scikit-learn, TensorFlow, PyTorch) required; SQL and data visualization tools are an advantage. - Strong analytical thinking, mathematical reasoning, and curiosity for research. - Clear written and verbal communication skills. In addition to the responsibilities and qualifications outlined above, you will have the opportunity to benefit from structured mentorship, regular feedback sessions, personalized career development coaching, and the chance to present ML research and projects to industry experts. Upon successful completion of the fellowship, you will receive a certificate and digital badge, paving the way for roles such as Machine Learning Engineer, Data Scientist, AI Research Associate, MLOps Engineer, or Applied AI Engineer. At HEPro, we offer a free experiential learning platform designed to help you level up your skills in management, tech, business, and process excellence. Through real-world projects, business models, certified courses, and live challenges, you can enhance your problem-solving abilities and gain job-ready skills. Join us at HEPro and embark on a journey of hands-on learning and growth. As an AI/ML Fellowship Associate at our organization, you will be part of a structured, project-based learning program aimed at developing foundational and intermediate skills in artificial intelligence, machine learning, and data science. Throughout the 6-8 month full-time fellowship, you will have the opportunity to 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. Key Responsibilities: - Project Execution & Documentation - Complete assigned AI/ML projects including exploratory data analysis, feature engineering, model building, and hyperparameter tuning. - Develop supervised and unsupervised machine learning models tailored to project requirements using Python libraries and ML frameworks such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. - 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 an