Data Scientist – AI & Machine Learning (3+ yrs)

About the role

We are looking for a highly skilled Data Scientist to join our innovative team in Jaipur. The role focuses on building and maintaining cutting‑edge AI systems, from classical machine learning to advanced generative models, while ensuring seamless user experiences.

Key responsibilities

• Develop generative AI solutions, including GANs, VAEs, and Transformer models.
• Design and implement classical ML models such as regression, decision trees, SVMs, random forests, gradient boosting, clustering, and dimensionality reduction.
• Train, fine‑tune, and deploy deep learning architectures like CNNs, RNNs, LSTMs, GANs, and Transformers.
• Participate in NLP projects, optimizing prompts for Large Language Models (GPT, BERT, T5).
• Perform data preprocessing, augmentation, and feature engineering on large datasets.
• Conduct hyper‑parameter optimization (grid search, random search, Bayesian optimization) and monitor model performance for drift, overfitting, and bias.
• Engage in code and design reviews to ensure scalable, high‑quality system architecture.
• Collaborate with cross‑functional teams to integrate ML solutions into production and stay current with AI research trends.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Data Science, or a related field.
• Minimum 3 years of professional experience in both classical machine learning and deep learning systems.
• Proven ability to translate complex mathematical problems into actionable ML models.

Required skills

• Generative AI techniques (GANs, VAEs, Transformers)
• Classical ML algorithms (regression, decision trees, SVM, random forest, gradient boosting, clustering, dimensionality reduction)
• Deep learning models (CNN, RNN, LSTM, GAN, Transformers)
• Natural Language Processing and Large Language Models (GPT, BERT, T5)
• Data preprocessing, augmentation, and feature engineering
• Hyper‑parameter optimization methods (grid search, random search, Bayesian optimization)
• Model evaluation metrics (accuracy, precision, recall, F1‑score)