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Mid-level ML role in metro with desirable generative-AI skills increases applicant competition moderately.
Specialized ML and enterprise data-integration skills are moderately transferable across industries.
Explicit 4–6 years requirement plus mandatory Python, RAG/LangChain, vector DB and NLP skills raises strictness.
Design and implement AI-native solutions addressing strategic business problems for customers, ensuring measurable value delivery.
Develop and deploy traditional ML models and Generative AI solutions including RAG pipelines, integrating enterprise data sources and managing vector databases.
Collaborate closely with data integration engineers and business analysts to troubleshoot, communicate results, and tailor AI models to business needs.
4-6 years of relevant work experience in AI/ML implementation.
Proficiency in Python scripting, data processing, model development, and integration in AI solution lifecycle.
Experience with traditional ML techniques and Generative AI frameworks such as RAG, LangGraph, and LangChain.
Bachelor’s degree in Engineering (preferably Computer Science); Master’s degree preferred.
Experienced in building ML solutions for supply chain use cases like forecasting and inventory reconciliation.
Able to operate effectively within a collaborative team environment involving cross-functional technical and business roles.
Strong expertise in advanced NLP applications and managing enterprise data integration within AI projects.