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