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Mid-level, metro AI role is popular but specialized healthcare and EHR requirements reduce applicant density.
Strong healthcare domain requirements (EHR, HIPAA, clinical data, provider experience) limit cross-industry transferability.
Explicit 2+ years plus mandatory healthcare provider, EHR, production ML, and compliance experience creates strict filters.
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Develop, fine-tune, and deploy large language models (LLMs) and multimodal AI systems integrating text, medical data, and images for healthcare applications.
Lead end-to-end AI development lifecycle including training, evaluation, productionalization, and optimization of models for accuracy, latency, and resource efficiency.
Ensure compliance with healthcare regulations (HIPAA, GDPR) and implement continuous learning and responsible AI practices within clinical and patient-facing environments.
Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or related field.
Minimum 2 years of hands-on experience in AI/ML development lifecycle, specifically with healthcare datasets and production pipelines in healthcare provider settings (e.g., hospitals, clinical systems).
Proficiency in Python and ML frameworks like PyTorch or TensorFlow; experience in training/fine-tuning LLMs and speech processing (STT/TTS) models.
Experience with healthcare data privacy compliance including HIPAA and GDPR; familiarity with real-world healthcare datasets and EHR-related domains.
Has practical experience building and deploying AI systems in clinical or operational healthcare environments, with exposure to EHR or related healthcare data.
Demonstrates expertise in multi-modal AI (text, speech, images) and conversational AI including voice assistant development for healthcare.
Operates effectively across cross-functional teams and Agile workflows, balancing AI technical depth with compliance, reproducibility, and ethical considerations in healthcare innovations.