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Tier-1 brand, Bengaluru metro, and mid-level profile amplify candidate competition.
Core GenAI and MLOps skills transfer across industries, though financial-services experience is preferred.
Explicit 5–8 year requirement plus mandatory GenAI, MLOps, and LLM skills make filters stringent.
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Own end-to-end delivery of production-grade AI applications including requirements, design, implementation, testing, deployment, and support.
Leverage generative AI techniques (prompt engineering, RAG, fine-tuning) and deep learning methods to solve client experience and business problems using multiple data sources (structured and unstructured).
Collaborate cross-functionally to translate ambiguous business problems into data-driven AI-enabled solutions and provide analytical insights impacting Treasury & Trade Services client experience and operations.
5 to 8 years of relevant experience in Data Science, including at least 2 years working on Generative AI solutions.
Hands-on experience with large language models, transformer architectures, prompt engineering, Retrieval-Augmented Generation (RAG), and model customization (fine-tuning, LoRA, PEFT).
Proficiency in frameworks such as PyTorch/TensorFlow, Hugging Face ecosystem, and knowledge of MLOps for GenAI including monitoring and deployment strategies.
Education: Masters degree preferred in Computer Science or Engineering.
Experienced in shipping AI-enabled products within agile environments focused on financial services client experience and operational improvements.
Strong technical expertise with generative AI models, deep learning, large language models, and practical application of complex AI techniques.
Capable of working independently on complex analytical problems with the ability to communicate and collaborate effectively across multiple teams and stakeholders.