Description

About Us Celebal Technologies is a premier software services company specializing in Data Science, Big Data, and Enterprise Cloud solutions. We empower organizations to unlock competitive advantages through intelligent, data-driven solutions powered by Artificial Intelligence and Machine Learning. Our expertise spans Robotics, AI, ML, and Generative AI, helping businesses achieve enhanced efficiency in today’s interconnected world. Job Description We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning and Python programming. The ideal candidate will be passionate about developing clean, efficient, and production-ready ML models and solving complex business problems through data-driven insights. Experience or exposure to Generative AI (GenAI) technologies is highly desirable. This role is ideal for someone who thrives in coding-intensive environments, enjoys experimenting with algorithms, and can translate theoretical concepts into scalable ML and AI solutions. Key Responsibilities Design, build, and optimize classical machine learning models such as regression, SVM, decision trees, random forests, gradient boosting, clustering, and dimensionality reduction. Write clean, efficient, and scalable Python code using libraries like NumPy, Pandas, and Scikit-learn. Implement and fine-tune deep learning models (CNNs, RNNs, Transformers) using TensorFlow, PyTorch, or similar frameworks. Work with Generative AI models including LLMs, Transformers, GANs, and VAEs; fine-tune models and contribute to prompt engineering and applied use cases. Handle large datasets by performing preprocessing, augmentation, and feature engineering to support ML workflows. Deploy models to production environments and monitor their performance, addressing issues such as model drift, bias, and overfitting. Maintain coding standards by participating in code reviews and ensuring scalability and maintainability of ML solutions. Collaborate effectively with cross-functional teams, including engineers and product managers, to integrate ML and GenAI solutions into real-world applications. Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or related fields. Strong expertise in Machine Learning and Python programming. Experience or good exposure to Generative AI technologies including LLMs, Transformers, prompt engineering, and fine-tuning. Proficient with ML/DL frameworks such as TensorFlow, PyTorch, or Keras. Solid mathematical and statistical foundation, including probability, optimization, and modeling techniques. Familiarity with cloud deployment platforms (AWS SageMaker, Azure ML, GCP AI) and MLOps practices is a plus. Demonstrated ability to write optimized, production-ready code with strong coding discipline. Soft Skills & Behavioral Competencies Strong analytical and problem-solving mindset. Excellent communication and teamwork skills. Self-motivated, detail-oriented, and reliable. Effective time management and ability to meet deadlines. Eagerness to learn and adapt to emerging technologies.

Role and Responsibilities

• Design, build, and optimize classical machine learning models such as regression, SVM, decision trees, random forests, gradient boosting, clustering, and dimensionality reduction. Write clean, efficient, and scalable Python code using libraries like NumPy, Pandas, and Scikit-learn. Implement and fine-tune deep learning models (CNNs, RNNs, Transformers) using TensorFlow, PyTorch, or similar frameworks. Work with Generative AI models including LLMs, Transformers, GANs, and VAEs; fine-tune models and contribute to prompt engineering and applied use cases. Handle large datasets by performing preprocessing, augmentation, and feature engineering to support ML workflows. Deploy models to production environments and monitor their performance, addressing issues such as model drift, bias, and overfitting. Maintain coding standards by participating in code reviews and ensuring scalability and maintainability of ML solutions. Collaborate effectively with cross-functional teams, including engineers and product managers, to integrate ML and GenAI solutions into real-world applications.