Junior ML Engineer

Jupiter AI Labs Inc; Dadri; Sur place; INR 600 000 - 1 200 000; Plein temps

Résumé du poste

Jupiter AI Labs Inc. is seeking an early‑career machine learning professional to build and deploy models alongside seasoned engineers. You will own data gathering, cleaning, feature engineering, and model training, while evaluating performance on unseen data.

You will work with Python, NumPy, Pandas, and Scikit-learn to preprocess data, train models, and interpret results, collaborating to refine architectures and training processes.

Qualifications

• 6 months–1 year of hands-on experience in ML/AI/data science roles or projects.
• Strong Python skills: writing functions, libraries, debugging, code structure.
• Solid foundation in ML concepts: supervised/unsupervised learning, regression, classification, clustering.
• Familiarity with NumPy, Pandas, and Scikit-learn for end-to-end modeling.
• Practical data preprocessing knowledge and model evaluation approaches.
• Analytical mindset, hypothesis testing, and iterative problem solving.

Responsabilités

• Build and deploy ML models with engineers, from inception to evaluation.
• Manage data pipelines: gather datasets, clean data, and prepare training information.
• Train neural nets and classical algorithms, validate, test on unseen data, measure performance.
• Document setup guides, model architecture details, and project reports.
• Collaborate with engineers and data scientists to review code and troubleshoot.
• Analyze model outputs to identify improvements in architecture or training.

Connaissances

Python

NumPy

Pandas

Scikit-learn

Data preprocessing

Model training

Model evaluation

Formation

Bachelor's or Master's in CS/IT/Data Science/AI/ML

Description du poste

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.