Intern - AI/ML

As a Machine Learning Engineer, your role involves collaborating with cross-functional teams to define ML problems and objectives. You will research, design, and implement machine learning algorithms and models such as supervised, unsupervised, deep learning, and reinforcement learning. Analyzing and preprocessing large-scale datasets for training and evaluation will be a key responsibility. You will also train, test, and optimize ML models for accuracy, scalability, and performance. Deploying ML models in production using cloud platforms and/or MLOps best practices is an essential part of your role. Monitoring and evaluating model performance over time to ensure reliability and robustness is crucial. Additionally, documenting findings, methodologies, and results to share insights with stakeholders is expected. 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 Company Details: The company values 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) is beneficial. Previous internships, academic research, or projects showcasing your ML skills are a plus. Familiarity with deployment frameworks like Docker and Kubernetes is considered good to have.,