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Tier‑1 brand, mid‑level ML role, metro location and popular LLM skills increase applicant competition.
Core ML/LLM skills transfer broadly, but finance risk/compliance preferences increase domain bias.
Explicit 6+ years, required ML/LLM experience and specific tech stack make shortlisting strict.
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Develop and maintain REST endpoints and integration code for AI/ML applications, focusing on LLM prompt engineering and debugging.
Write and optimize SQL queries and code to support LLM context retrieval and inference.
Ensure code compliance with risk restrictions and document work for reusability and compliance, including unit and integration testing for LLMs.
6+ years of total professional experience, with at least 3 years in data science or machine learning engineering roles.
Proficient in Python with experience in FastAPI or Flask and libraries like LangChain, Pandas, Numpy, SpaCy, NLTK.
Experience in the full data pipeline: data cleaning, feature engineering, model training, evaluation, and production deployment.
Bachelor’s degree or equivalent experience; SQL skills mandatory.
Experienced in end-to-end ML system development including model retraining and deployment in production environments.
Technical proficiency in prompt engineering, LLM inference parameter tuning, and integrating external search libraries for large language models.
Comfortable working in a technology-driven application development team with focus on AI/ML and compliance standards.