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Mid-level Bangalore role with hybrid option and general ML title, but strong fraud/LLM specialization moderates competition.
High—requires banking fraud domain knowledge, regulatory familiarity, and specialized graph/LLM skills.
Explicit 4+ years requirement plus mandatory banking fraud, LLM, and MLOps expertise.
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Develop and maintain advanced fraud detection, credit risk, AML, and transaction anomaly models using supervised and unsupervised techniques.
Design, fine-tune, and implement generative AI models (LLMs, multimodal pipelines) for banking-specific tasks including SAR generation, dispute summarisation, and customer communication.
Operationalize AI/ML models with MLOps tools, ensure regulatory compliance, and collaborate with cross-functional teams in Agile squads to deliver banking analytics solutions.
Minimum 4+ years of professional experience in data science with fraud analytics or banking domain focus.
Proven expertise building and deploying fraud detection models, including experience with graph analytics and familiarity with banking data schemas and regulations (RBI, PCI-DSS, FATF AML/CFT).
Strong programming skills in Python, experience with ML frameworks (PyTorch/TensorFlow), and cloud platforms (GCP, AWS, or Azure).
Deep knowledge of generative AI, LLM fine-tuning, RAG systems, vector DBs, and MLOps tooling for model lifecycle management.
Experienced data scientist with a strong background in banking fraud detection and risk modelling, able to deliver production-ready AI solutions in regulated environments.
Skilled in integrating advanced AI technologies including generative AI and multimodal ML into real-time banking systems with attention to model interpretability and auditability.
Practitioner accustomed to Agile consulting environments who can communicate complex model insights effectively to varied stakeholders including compliance and leadership teams.