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Design, train, fine-tune, and deploy production-ready LLM and multimodal AI systems for healthcare. Build voice-enabled conversational interfaces, preprocess and manage large healthcare datasets with privacy controls, implement continuous learning and evaluation pipelines, and ensure model fairness, reliability, and integration into clinical products through cross-functional collaboration and Agile practices.

The Senior AI QA Engineer will design robust testing frameworks for validating clinical pipeline outputs and ensure data integrity through advanced QA practices in AI/ML.

Lead architecture and development of cloud-native, AI-first healthcare platforms. Drive AI-augmented engineering, build scalable microservices and APIs (FHIR/HL7/CDA), adopt LLMs/Agentic AI and AI tooling, ensure security/HIPAA compliance, mentor engineers, and collaborate across product, QA, DevOps, and compliance to improve velocity and quality.

Design, build, and maintain cloud-native and on-premise healthcare applications and microservices. Develop RESTful APIs, responsive frontends, containerized deployments (Docker/Kubernetes), CI/CD pipelines, and AI-powered features using LLMs. Ensure security, compliance (HIPAA), and interoperability (HL7/FHIR/CDA); collaborate across product, QA, and clinical teams; participate in architecture, code reviews, and mentoring.

The Principal Engineer will lead AI solution development, focusing on Large Language Models and multimodal AI systems, ensuring technical excellence and cross-functional collaboration in healthcare technology.

Lead architecture, development, and deployment of production-grade healthcare AI: LLMs, multimodal models, STT/TTS, and agentic systems. Build MLOps/CI-CD on Azure, ensure observability, compliance (HIPAA/GDPR), mentor engineers, and collaborate with product and clinical teams.