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
