Apprentice-IT

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

• 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)

• 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