AI/ML Fellowship Program (New Delhi)

Role Overview: As an AI/ML Fellowship Associate, you will participate in 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 months full-time fellowship, you 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. Key Responsibilities: - Project Execution & Documentation - Complete assigned AI/ML projects including exploratory data analysis, feature engineering, model building, and hyperparameter tuning. - Build supervised and unsupervised machine learning models tailored to project requirements using Python libraries and ML frameworks. - Document datasets used, feature engineering choices, model performance, and deployment workflows. - Research & Framework Development - Research AI/ML best practices, MLOps methodologies, and state-of-the-art algorithms. - Propose enhancements to models, feature engineering approaches, and ML workflows to improve performance and scalability. - 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. - Explicit written and verbal communication skills. Company Details: HEPro is a free experiential learning platform offering structured mentorship from AI/ML professionals, opportunities to present ML research to industry experts, and a pathway to roles such as Machine Learning Engineer, Data Scientist, AI Research Associate, MLOps Engineer, or Applied AI Engineer. Role Overview: As an AI/ML Fellowship Associate, you will participate in 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 months full-time fellowship, you 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. Key Responsibilities: - Project Execution & Documentation - Complete assigned AI/ML projects including exploratory data analysis, feature engineering, model building, and hyperparameter tuning. - Build supervised and unsupervised machine learning models tailored to project requirements using Python libraries and ML frameworks. - Document datasets used, feature engineering choices, model performance, and deployment workflows. - Research & Framework Development - Research AI/ML best practices, MLOps methodologies, and state-of-the-art algorithms. - Propose enhancements to models, feature engineering approaches, and ML workflows to improve performance and scalability. - 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, Math