Machine Learning Engineer – Data Science & AI

Role Overview

As a ML Engineer, you'll work alongside experienced data scientists and ML engineers to build, train, and deploy machine learning models. You'll get hands-on exposure to the full ML lifecycle — from data wrangling to production deployment.

Responsibilities

• Assist in building and evaluating ML/DL models for various business use cases
• Perform exploratory data analysis (EDA) and feature engineering on real datasets
• Write clean, efficient Python code for data pipelines and model training
• Experiment with different algorithms and track results systematically
• Collaborate with the engineering team to deploy models into production
• Stay updated with the latest research and bring relevant ideas to the team

Requirements

• Strong foundation in Python and its data ecosystem (NumPy, Pandas, Scikit-learn)
• Understanding of core ML concepts — regression, classification, clustering, model evaluation
• Familiarity with at least one deep learning framework (TensorFlow or PyTorch)
• Basic knowledge of statistics and probability
• Comfortable working with data — cleaning, transforming, and visualizing it
• Familiarity with Git and version control
• B.Tech / B.E. / B.Sc. in Computer Science, Data Science, Mathematics, Statistics, or a related field

Nice to Have

• Experience with NLP, computer vision, or time series problems
• Exposure to MLOps tools (MLflow, DVC, or similar)
• Familiarity with cloud ML services (AWS SageMaker, GCP Vertex AI, etc.)
• Knowledge of SQL and working with structured data

Benefits

• Fully remote work
• Health insurance
• Provident Fund
• Mentorship and fast-track growth

Skills

• Python
• Scikit-learn
• PyTorch
• TensorFlow
• SQL