Apprentice-IT

## What you\u2019ll do:\n\nWe are looking for a motivated AI/ML Engineering graduate to join our Artificial Intelligence and Machine Learning (AIML) team. This role is ideal for a fresher with a strong academic foundation in AI/ML who is eager to apply theory to real world business problems under mentorship. \nYou will work closely with senior AI/ML engineers, data scientists, and platform teams to build, experiment with, and operationalize machine learning solutions on enterprise scale data platforms.\n\n\u2022 Assist in building and training machine learning models for structured and unstructured data use cases \n\u2022 Perform data analysis, preprocessing, and feature engineering on large datasets \n\u2022 Support experimentation using AutoML and custom ML approaches \n\u2022 Evaluate model performance and assist in tuning for accuracy and robustness \n\u2022 Work with AI/ML platforms and tools for model development and experimentation \n\u2022 Collaborate with engineers and analysts to understand business problems and translate them into ML tasks \n\u2022 Document experiments, learnings, and model outcomes clearly \n\u2022 Follow best practices for responsible AI, data governance, and security\n\n## Qualifications:\n\n\u2022 Bachelor\u2019s degree in Engineering (B.E./B.Tech) with specialization in: \no Artificial Intelligence \no Machine Learning \no Data Science \no Computer Science (with strong AI/ML coursework)\n\n## Skills:\n\n\u2022 Strong fundamentals in: \no Machine Learning algorithms \no Statistics and linear algebra \no Data structures and basic algorithms \n\u2022 Working knowledge of Python \n\u2022 Familiarity with ML libraries such as: \no scikit learn \no TensorFlow or PyTorch (basic exposure is sufficient) \n\u2022 Basic understanding of SQL and working with datasets\n\nGood to Have (Not Mandatory) \n\u2022 Exposure to: \no Cloud platforms (Azure / AWS / GCP) \no Data platforms like Snowflake \no ML lifecycle concepts (training, evaluation, deployment) \n\u2022 Academic or personal projects involving: \no Predictive modeling \no NLP or computer vision \no Time series forecasting \n\u2022 Familiarity with notebooks, Git, or basic MLOps concepts\n\n \nWhat You Will Learn \n\u2022 End to end AI/ML use case development in an enterprise environment \n\u2022 Working with real production scale datasets \n\u2022 Model experimentation, evaluation, and promotion practices \n\u2022 AI/ML platform tools and best practices \n\u2022 How ML solutions are governed, monitored, and scaled\n