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Tier-1 brand, generalist ML title, mid-level experience, and metro location increase applicant competition.
Core ML skills are transferable, but enterprise RAG and platform experience favors similar-industry candidates.
Explicit 2–4 year requirement plus required ML tooling and domain skills create moderate filtering.
Support development, testing, and deployment of AI/ML models including forecasting, predictive models, and Generative AI use cases.
Contribute to Generative AI workflows such as prompt development, document ingestion, embeddings, and retrieval in Retrieval-Augmented Generation (RAG) setups.
Collaborate with software engineers and stakeholders to integrate, monitor, and improve AI/ML models across multiple business units using tools like Databricks and AWS SageMaker.
Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field.
2–4 years of experience in data science, analytics, or machine learning roles, or equivalent academic/project experience.
Proficiency in Python and data science libraries (pandas, NumPy, scikit-learn).
Experience with SQL databases and cloud/data platforms such as Databricks or AWS.
Experienced in agile or Scrum environments and capable of collaborating cross-functionally with software engineers and stakeholders.
Familiar with Generative AI concepts including LLM APIs and Retrieval-Augmented Generation.
Able to build scalable, reusable AI/ML models for business forecasting, recommendation systems, and related use cases.