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Tier-1 brand, metro location, and popular mid-level AI role increase candidate competition.
ML/LLM engineering skills are broadly transferable, but finance governance raises medium sensitivity.
Explicit 3–8 years, mandatory LLM/production experience, and governance requirements make screening strict.
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Build, integrate, and operationalize AI solutions, including machine learning, NLP, GenAI, and LLM-based capabilities, across internal platforms and client-facing products.
Design and maintain scalable, reliable AI applications that support multiple MSCI business verticals such as ESG, Index, Analytics, and Real Estate.
Ensure production readiness and compliance of AI systems with enterprise standards and AI governance including security, transparency, and regulatory requirements.
3–8 years of experience in data science, AI engineering, or applied machine learning roles.
Bachelor’s or Master’s degree in Computer Science, Mathematics, Engineering, or related quantitative field.
Strong hands-on Python experience and familiarity with machine learning, NLP, LLMs, LangChain, LangGraph, RAG, or agentic AI systems.
Experience delivering AI/ML solutions into production environments with software engineering best practices.
Experienced AI engineer comfortable working hands-on with advanced AI technologies like LLMs and agentic AI frameworks in production settings.
Capable of collaborating cross-functionally with product, QA, data operations, and IT teams to integrate AI into enterprise platforms.
Operationally strong with focus on engineering excellence, compliance, and production-grade AI solutions in regulated or financial services environments.