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:
• Artificial Intelligence
• Machine Learning
• Data Science
• Computer Science (with strong AI/ML coursework)

Skills

• Strong fundamentals in:
• Machine Learning algorithms
• Statistics and linear algebra
• Data structures and basic algorithms
• Working knowledge of Python
• Familiarity with ML libraries such as:
• scikit learn
• TensorFlow or PyTorch (basic exposure is sufficient)
• Basic understanding of SQL and working with datasets Good to Have (Not Mandatory)
• Exposure to:
• Cloud platforms (Azure / AWS / GCP)
• Data platforms like Snowflake
• ML lifecycle concepts (training, evaluation, deployment)
• Academic or personal projects involving:
• Predictive modeling
• NLP or computer vision
• 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