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Tier-1 brand, mid-level ML/AI role, metro location, and broad skillset requirements drive high competition.
Core ML/NLP skills transfer, but healthcare domain and regulatory experience increase domain specificity.
Explicit years, required ML/NLP, LLM, cloud, MLOps, and production experience create strict filtering.
Develop, train, and optimize NLP and machine learning models for clinical document understanding, information extraction, and conversational AI in healthcare.
Build scalable data pipelines, implement production-ready AI services/APIs, and collaborate with software engineering teams to integrate AI capabilities into products.
Deploy and monitor AI models using MLOps, ensure compliance with healthcare quality, privacy, security, and regulatory requirements.
Master's degree with 5+ years or PhD with 2+ years experience in Computer Science, Data Science, AI/ML, Computational Linguistics, or related fields.
Strong experience in NLP, Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Information Extraction, Named Entity Recognition (NER), and Text Analytics.
Hands-on proficiency with Python, PyTorch/TensorFlow, Hugging Face, NLTK/spaCy, and modern AI frameworks.
Experience with cloud-native applications, REST APIs, microservices, CI/CD, Docker, Kubernetes, AWS/Azure/GCP, and MLOps best practices.
Proven ability to move AI solutions from research stage to production in regulated healthcare environments.
Experience working cross-functionally with product managers, clinical experts, and engineers to deliver impactful AI healthcare products.
Technical leadership or mentoring experience, especially in healthcare AI or clinical NLP domains.