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Strong Tier-1 brand, Bangalore location, and broad GenAI/MLOps requirements increase candidate competition.
Core ML, GenAI and MLOps skills transfer across industries, though payments experience increases domain specificity.
Explicit 7–10 years requirement plus mandatory ML, GenAI and MLOps stack enforces strict shortlisting.
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Own end-to-end development and deployment of ML and Generative AI models focused on payment datasets (credit card, UPI) for fraud detection, customer segmentation, and demand forecasting.
Apply and fine-tune open-source LLMs for synthetic data generation, transaction summarization, and conversational AI in payments domain.
Lead collaboration with cross-functional teams to deliver business-impacting AI solutions and manage production ML pipelines including monitoring model performance and drift.
7 to 10 years of experience as a Machine Learning Engineer or Data Scientist.
Proficiency in Python, R, Java; ML frameworks including TensorFlow, PyTorch, Scikit-learn, and OSS LLMs (Hugging Face Transformers, Llama, GPT-J, Falcon).
Experience with data processing tools like SQL, Spark, Hadoop and ML deployment tools such as MLflow, Kubeflow, Docker, Kubernetes.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field; Location: Bengaluru.
Deep expertise in ML model development, evaluation, and optimization using real-world payment transaction data.
Experience implementing and deploying GenAI and foundation models specifically for payment-related NLP tasks and synthetic data generation.
Ability to lead technical collaboration and communicate complex AI concepts effectively across technical and business stakeholders.