Apprentice-IT

## What you’ll do: We 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. You 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. • Assist in building and training machine learning models for structured and unstructured data use cases • Perform data analysis, preprocessing, and feature engineering on large datasets • Support experimentation using AutoML and custom ML approaches • Evaluate model performance and assist in tuning for accuracy and robustness • Work with AI/ML platforms and tools for model development and experimentation • Collaborate with engineers and analysts to understand business problems and translate them into ML tasks • Document experiments, learnings, and model outcomes clearly • Follow best practices for responsible AI, data governance, and security ## Qualifications: • Bachelor’s degree in Engineering (B.E./B.Tech) with specialization in: o Artificial Intelligence o Machine Learning o Data Science o Computer Science (with strong AI/ML coursework) ## Skills: • Strong fundamentals in: o Machine Learning algorithms o Statistics and linear algebra o Data structures and basic algorithms • Working knowledge of Python • Familiarity with ML libraries such as: o scikit learn o TensorFlow or PyTorch (basic exposure is sufficient) • Basic understanding of SQL and working with datasets Good to Have (Not Mandatory) • Exposure to: o Cloud platforms (Azure / AWS / GCP) o Data platforms like Snowflake o ML lifecycle concepts (training, evaluation, deployment) • Academic or personal projects involving: o Predictive modeling o NLP or computer vision o Time series forecasting • Familiarity with notebooks, Git, or basic MLOps concepts What You Will Learn • End to end AI/ML use case development in an enterprise environment • Working with real production scale datasets • Model experimentation, evaluation, and promotion practices • AI/ML platform tools and best practices • How ML solutions are governed, monitored, and scaled