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Mid-level, generalist ML role with broad required stack increases applicant density.
Healthcare compliance and EHR integration requirements make cross-industry transfers difficult.
Explicit 6+ years plus specific ML stack, cloud, and healthcare compliance requirements raise screening rigor.
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Design, develop, and deploy AI/ML models to improve clinical decision-making, operational efficiency, and patient outcomes.
Collaborate with cross-functional teams to integrate AI capabilities into scalable, secure, and compliant SaaS healthcare platforms.
Own AI strategy focusing on ethical design, bias mitigation, explainability, and alignment with clinical and business goals.
Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
6+ years experience in AI/ML engineering, preferably in healthcare SaaS environments.
Proven experience deploying AI/ML models in production and integrating with cloud-native platforms (Azure, AWS, GCP).
Knowledge of healthcare compliance standards like HIPAA, HL7, FHIR.
Experienced in building AI/ML solutions embedded in clinical workflows or healthcare SaaS.
Proficient in advanced ML techniques (CNNs, RNNs, transformers, generative models) and ML frameworks like TensorFlow, PyTorch.
Technical skills spanning data engineering, cloud environments, and software engineering with a focus on scalable, compliant platforms.