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Mid-level ML role in Bangalore with a common title and metro hiring amplifies competition.
Strong domain bias due to banking fraud, regulatory and graph analytics requirements.
Explicit 4+ years requirement plus numerous mandatory ML, banking and MLOps skills tighten shortlisting.
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Develop, validate, and operationalize fraud detection and credit risk scoring models integrating real-time banking event streams.
Design and fine-tune LLMs and build Retrieval-Augmented Generation systems for banking-specific applications including SAR narrative generation and fraud rulebook knowledge bases.
Write production-quality Python code, apply MLOps for model lifecycle management, and engage in Agile delivery with stakeholder communication on model interpretability and compliance.
Minimum 4 years of relevant experience in fraud and banking analytics and AI/ML.
Proven expertise in fraud modeling covering card-not-present, account takeover, synthetic identity, and money-mule networks.
Strong skills in supervised and unsupervised ML methods, Computer Vision, NLP, and proficiency with PyTorch or TensorFlow.
Mandatory domain knowledge of banking data schemas, regulatory frameworks (RBI, PCI-DSS, AML), and hands-on experience with generative AI models and engineering in Python.
Experienced in building complex fraud detection solutions with graph analytics and advanced AI techniques within banking domain constraints.
Ability to design and deploy scalable AI systems integrating generative AI, RAG architectures, and MLOps tooling in cloud environments.
Comfortable translating technical model insights into clear narratives for compliance, risk, and business stakeholders in Agile, client-facing settings.