Intern - AI/ML

As a Machine Learning Engineer, you will work closely with cross-functional teams to define ML problems and objectives. You will be responsible for researching, designing, and implementing various machine learning algorithms and models such as supervised, unsupervised, deep learning, and reinforcement learning. Your tasks will also include analyzing and preprocessing large-scale datasets for training and evaluation, as well as training, testing, and optimizing ML models to ensure accuracy, scalability, and performance. Deploying ML models in production using cloud platforms and/or MLOps best practices will be a key part of your role. You will monitor and evaluate model performance over time to ensure reliability and robustness. Additionally, documenting findings, methodologies, and results to share insights with stakeholders will be crucial in this position. Key Responsibilities: - Collaborate with cross-functional teams to define ML problems and objectives - Research, design, and implement machine learning algorithms and models - Analyze and preprocess large-scale datasets for training and evaluation - Train, test, and optimize ML models for accuracy, scalability, and performance - Deploy ML models in production using cloud platforms and/or MLOps best practices - Monitor and evaluate model performance over time - Document findings, methodologies, and results to share insights with stakeholders Qualifications Required: - Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (graduation within the last 12 months or upcoming) - Proficiency in Python or a similar language, with experience in frameworks like TensorFlow, PyTorch, or Scikit-learn - Strong foundation in linear algebra, probability, statistics, and optimization techniques - Familiarity with machine learning algorithms and concepts like feature engineering, overfitting, and regularization - Hands-on experience working with structured and unstructured data using tools like Pandas, SQL, or Spark - Ability to think critically and apply your knowledge to solve complex ML problems - Strong communication and collaboration skills to work effectively in diverse teams Additional Details: The company values additional skills such as experience with cloud platforms (e.g., AWS, Azure, GCP) and MLOps tools (e.g., MLflow, Kubeflow). Knowledge of distributed computing or big data technologies (e.g., Hadoop, Apache Spark), previous internships, academic research, or projects showcasing ML skills, and familiarity with deployment frameworks like Docker and Kubernetes are considered good to have.,