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