AI/ML Fellowship Program

Job Description Position Title:

AI/ML Fellowship Associate Program Duration:

6 – 8 Months (Full-Time Fellowship)

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 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. Learning Outcomes Develop proficiency in the end-to-end AI/ML lifecycle: data preparation, model development, performance evaluation, deployment, and monitoring. Gain hands-on experience with supervised/unsupervised learning, deep learning fundamentals, and applied MLOps workflows. Strengthen scientific research thinking, experiment design, model optimization, and technical communication skills. Build a portfolio showcasing ML models, analytics case studies, deployment pipelines, and AI-driven solutions — demonstrating job readiness for AI/ML roles. 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. Program Benefits Structured mentorship from AI/ML professionals, regular feedback sessions, and personalized career development coaching. Opportunities to present ML research and projects to industry experts and receive professional evaluations. Fellowship certificate and digital badge upon successful completion. Accelerated pathway to roles such as Machine Learning Engineer, Data Scientist, AI Research Associate, MLOps Engineer, or Applied AI Engineer.

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