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Tier-1 brand, metro location, and mid-level generalist ML role create high candidate competition.
Core ML/LLM skills are transferable, but shipping logistics domain knowledge adds moderate bias.
Explicit six-year requirement and mandatory production ML/LLM experience create strict shortlisting filters.
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Build and deploy advanced machine learning, deep learning, and AI models focused on optimizing shipping logistics at global scale.
Collaborate cross-functionally with product managers, engineers, and researchers to operationalize data-driven solutions enhancing the shipping experience for millions of users.
Own model evaluation and metrics definition for ML-driven taxonomy and classification models in a high-volume, low-latency production environment.
6 years of experience in analytics or data science roles.
Proficiency in SQL and Python for data manipulation, analytics, and automation.
Experience deploying and operating Machine Learning, Deep Learning, NLP, and Large Language Models at production scale.
Work Experience Required: 6 Years
Demonstrated expertise in building and deploying LLMs, prompt engineering, and retrieval-augmented generation (RAG) techniques for AI insights.
Comfortable working in fast-paced, iterative environments with strong cross-functional collaboration and high agency.
Strong analytical mindset capable of translating complex data findings into actionable recommendations for diverse teams.