Key Responsibilities

Model Development & Optimization

• Design, develop, and deploy advanced deep learning models (CNN, RNN, LSTM, Transformers)
• Build scalable AI solutions for real-world business problems
• Perform feature engineering, hyperparameter tuning, and model optimization
• Implement transfer learning and fine-tuning of pre-trained models
• Improve model accuracy, efficiency, and scalability

Data Engineering & Processing

• Work with large structured and unstructured datasets
• Develop data pipelines for training and inference workflows
• Perform exploratory data analysis (EDA) and statistical analysis
• Ensure data quality, validation, and governance

Deployment & MLOps

• Deploy models into production using APIs and containerization tools
• Work with Docker, Kubernetes, and CI/CD pipelines
• Monitor model performance and retrain models when necessary
• Collaborate with DevOps teams for scalable deployment

AI Frameworks & Technologies

• Strong hands-on experience with Python
• Expertise in TensorFlow and/or PyTorch
• Experience with Scikit-learn, NumPy, Pandas
• Exposure to cloud platforms (AWS / Azure / GCP)
• Knowledge of distributed computing frameworks is an added advantage

Collaboration & Leadership

• Work closely with product managers, engineers, and stakeholders
• Translate business problems into data science solutions
• Mentor junior data scientists and review code/models
• Present insights and model results to technical and non-technical audiences

Required Skills & Experience

• 6+ years of experience in Data Science / Machine Learning
• Strong expertise in Deep Learning architectures (CNN, RNN, LSTM, Transformers)
• Experience in model deployment and MLOps practices
• Solid understanding of statistics, probability, and linear algebra
• Experience handling large-scale datasets
• Strong problem-solving and analytical skills