Junior ML Engineer

About this role

Batch: 2024/2025/2026. About the role: - Build and deploy machine learning models alongside experienced engineers, taking on core technical responsibilities from inception through evaluation. - Handle the full pipeline of data work: gathering datasets, removing errors and inconsistencies, exploring patterns, and preparing information for model training. - Train neural networks and classical algorithms, run validation checks, test on unseen data, and measure how well models perform using standard metrics. - Write and maintain clear technical documentation, including setup guides, model architecture details, and project reports that track progress and findings. - Partner with other engineers and data scientists to design solutions, review code, and troubleshoot performance issues together. - Read model outputs, identify why performance falls short, and suggest refinements to the architecture or training process. What you'll work on: - Python is your primary tool; you'll write clean, readable code that trains models, processes data, and builds pipelines. - NumPy, Pandas, and Scikit-learn are your daily companions for numerical computing, data manipulation, and traditional machine learning. - Preprocessing steps such as normalization, handling missing values, feature engineering, and splitting data for training and testing. - Model evaluation frameworks: confusion matrices, accuracy, precision, recall, cross-validation, and interpreting results to spot overfitting or underfitting. - Datasets ranging from structured tables to exploratory problems where you'll apply domain knowledge and statistical thinking. What we're looking for: - 6 months–1 year of hands-on experience in machine learning, artificial intelligence, or data science roles or projects. - Strong grasp of Python: writing functions, working with libraries, debugging, and structuring code. - Solid foundation in machine learning concepts: supervised and unsupervised learning, regression, classification, clustering, and when to apply each. - Comfort with NumPy for array operations, Pandas for data frames, and Scikit-learn for building models end-to-end. - Practical knowledge of data preprocessing techniques and how to evaluate whether a model is actually solving the problem. - Sharp analytical mind and ability to break down complex problems, test hypotheses, and iterate on solutions. - Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Machine Learning, or equivalent technical field.