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Tier-1 brand, Bengaluru metro location, and popular ML/GenAI title create high candidate density despite niche payments focus.
Core ML/GenAI skills are transferable, but payments domain experience and production MLOps increase industry specificity.
Explicit 7–10 years requirement plus mandatory ML/LLM, MLOps, and production deployment skills raise shortlisting strictness.
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Lead data handling and feature engineering on payment datasets (credit card, UPI) to extract features for ML models.
Develop, fine-tune, and deploy machine learning and generative AI models for fraud detection, customer segmentation, demand forecasting, and NLP tasks using TensorFlow, PyTorch, Scikit-learn, and open-source LLMs.
Manage production deployment and monitoring of ML/GenAI models with tools like MLflow, Kubeflow, Docker, and Kubernetes ensuring model performance and reliability.
7 to 10 years of experience as a Machine Learning Engineer or Data Scientist.
Proficiency in Python, R, and Java programming languages and data processing tools such as SQL, Spark, and Hadoop.
Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and OSS LLMs (Hugging Face Transformers, Llama, GPT variants).
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field, Location: Bengaluru.
Experienced in applying machine learning techniques to payment domain data, specifically credit card and UPI datasets for fraud detection and transaction analytics.
Skilled in implementing and fine-tuning generative AI foundation models for NLP use cases including synthetic data generation and conversational AI.
Capable of end-to-end ML lifecycle management including hyperparameter tuning, distributed training, model deployment, and monitoring in production environments.