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Tier-1 brand, mid-level ML role, metro context, and broad GenAI/NLP skillset increase competition.
Core ML skills are transferable but healthcare domain knowledge and regulated data practices increase specificity.
Multiple explicit years plus deep ML, distributed training, GenAI optimization and domain-specific skills enforce strict screening.
Lead design and development of NLP, ML & AI healthcare products focusing on text processing, NLU, SLU, and computer vision.
Manage full machine learning lifecycle including data preparation, model development, deployment, and performance tracking for healthcare applications.
Drive technical decisions and standards in AI/ML applied science, including running complex proofs-of-concept and optimizing GenAI models.
Master's degree in computer science or related field with 4+ years industry experience, or PhD with 2+ years industry experience in data science or machine learning.
4+ years experience with deep learning frameworks and distributed training; 3+ years in ML and DL modeling; 2+ years applying computational algorithms to structured and unstructured data.
Minimum 1 year experience in GenAI model optimization and Agentic AI.
Proven communication, writing, and presentation skills.
Experienced hands-on AI/ML scientist skilled in NLP, NLU, SLU, and computer vision technologies within healthcare context.
Capable of independently leading end-to-end machine learning projects and managing technology prioritization in product development.
Demonstrates ability to engage in technical design discussions and mentor peers on complex scientific issues.