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 robust 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
Solid 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

Positive 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

📌 Apprentice It Pune (India)
🏢 Eaton
📍 India