Responsibilities: • Design, build, and evaluate machine learning and deep learning models for classification, regression, recommendation, NLP, computer vision, and time-series forecasting. • Apply deep learning techniques (e.g., CNNs, RNNs, LSTMs, Transformers) to solve complex, data-intensive problems. • Lead the development of ML products, from model prototyping through production deployment, performance monitoring, and continuous improvement. • Select appropriate architectures and hyperparameters, optimize model performance, and use proper evaluation metrics (e.g., AUC, F1, BLEU, IoU, perplexity) based on the use case. • Collaborate with product managers and engineers to translate business challenges into deployable solutions using AI/ML. • Design automated pipelines for data preprocessing, feature engineering, training, and inference (batch or real-time). • Evaluate model drift, monitor performance post-deployment, and implement retraining pipelines as part of a production MLOps system. • Mentor junior data scientists, contribute to code reviews, and lead technical discussions across the data science and engineering teams. Requirements: • Bachelor’s degree in Computer Science, Statistics, Applied Math, or related field (Master’s or PhD strongly preferred). • 5+ years of industry experience in applied machine learning, with 2+ years focused on deep learning and neural network applications. • Experience in Banking, Payments or Financial Services formulating AI data solutions that allow us to leverage our data to know our customers better and target our resources for better market penetration and focused attention and education. • Proficiency in Python and ML libraries such as scikit-learn, XGBoost, TensorFlow, Keras, or PyTorch. • Deep understanding of neural networks, model regularization, overfitting/underfitting prevention, and GPU-accelerated training. • Experience with customer data enrichments. • Proven track record of building, evaluating, and deploying machine learning models at scale in production environments. • Experience with cloud platforms (AWS/GCP/Azure), containerization, and model serving technologies. • Excellent communication skills, with the ability to present complex findings to both technical and non-technical stakeholders. • Hands-on experience with real-world applications of deep learning, such as recommendation engines, fraud detection, customer segmentation, document summarization, image recognition, or speech processing. • Familiarity with MLOps tools (e.g., MLflow, SageMaker, Airflow, Kubeflow). • Experience with CI/CD for ML, feature stores, and real-time inference systems. • Contributions to academic research, open-source ML projects, or ML/AI patents