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Mid-level, popular data scientist title with metro location and 3–5 year experience increases applicant competition.
ML engineering and LLM skills are broadly transferable, though financial compliance adds some domain specificity.
Explicit 3–5 years plus mandatory NLP/generative AI, Python, Docker, and cloud skills create moderate filtering.
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Build and validate living agentic AI systems within financial workflows using frameworks like LangGraph, Cortex, and AgentCore.
Develop evaluation frameworks and gold-standard datasets to measure generative AI output performance and accuracy.
Collaborate with engineers and investment professionals to integrate AI models and build scalable AI tools with high data quality and compliance standards.
3-5 years of experience in data science or machine learning with focus on NLP and Generative AI.
Proficiency in Python, cloud-native development, Docker, and CI/CD.
Exposure or strong interest in LangGraph, LangChain, Copilot Studio, or AgentCore frameworks.
Workplace model requires hybrid work from Hyderabad office (at least 3 days/week onsite).
Experience working at the intersection of data engineering and full stack development with rapid iteration cycles.
Ability to design complex multi-agent AI workflows and implement systematic testing for financial compliance.
Strong capability in building evaluation metrics and datasets for AI model validation and testing in financial services context.