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Tier-1 bank, metro location, mid-level AI/data science role with broad skills drives high competition.
Requires specialized banking risk, controls and data governance knowledge, reducing cross-industry transferability.
Explicit 5–8 years and mandatory ML/LLM, big data, and banking controls increase shortlisting strictness.
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Design, develop, and implement AI-powered solutions leveraging large language models (LLMs) and machine learning to solve complex business problems.
Create, refine, and optimize prompts for generative AI tools, integrating AI into products and workflows across teams.
Monitor and analyze AI model performance, provide insights, develop algorithms, manage data quality, and collaborate with risk, engineering, and business stakeholders.
Bachelor’s degree in quantitative field such as computer science, engineering, mathematics, machine learning, or statistics.
5-8 years of experience in a data science role with specialization in AI, LLM, and prompt engineering.
Proficiency in Python and relevant ML libraries (Numpy, Pandas, Scikit-learn), plus experience with AI frameworks like TensorFlow, PyTorch, OpenAI, and LangChain.
Experience with big data platforms (Hadoop, Spark), SQL databases, and demonstrated ability to write high-quality code and develop ML models.
Experienced in applying advanced AI/ML techniques, specifically LLMs and prompt engineering, to drive automated and scalable solutions in financial services domain.
Strong collaboration skills with cross-functional teams including product, engineering, risk management, and data engineering.
Good domain understanding of banking (Wealth, Cards, Deposits, Loans, Insurance) and business risk, controls, and compliance relevant to data science projects.