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Opportunity Get Well is seeking a talented and innovative Senior AI Engineer to lead the development and deployment of advanced AI solutions within a mission-driven healthcare environment. This role is ideal for a seasoned professional with hands-on experience in machine learning (ML), large language models (LLMs), and generative AI (GenAI), with a strong focus on productionizing scalable and reliable models. As a Senior AI Engineer, you will drive end-to-end AI development—from ideation and training data pipelines to model deployment and continuous improvement—while addressing unique healthcare data challenges. You’ll collaborate across functions including product, engineering, data science, and clinical experts to deliver high-impact AI applications, including voice assistants and multimodal systems tailored to real-world care settings. Responsibilities AI Model Development & Customization Fine-tune SLMs / LLMs and develop ML models for healthcare-specific use cases. Build multimodal AI systems that integrate text, structured medical data, and images. Optimize models for accuracy, latency, and resource efficiency in production settings. Evaluate and integrate speech-to-text (STT) and text-to-speech (TTS) models into conversational interfaces. Customize foundation models using domain adaptation and prompt engineering techniques (e.g., PEFT, LoRA). Develop AI algorithms from healthcare data and guide them through a full production lifecycle into deployed solutions. Productionization & Deployment Lead model deployment end-to-end with cloud-native tools and infrastructure (Azure ML). Implement model performance monitoring, drift detection, and auto-retraining pipelines. Ensure AI solutions are scalable, reliable, and aligned with security and compliance requirements in healthcare environments. Technical Leadership Provide technical guidance to junior AI engineers. Conduct design reviews and drive the adoption of best practices in model reproducibility, validation, and explainability. Lead the evaluation and integration of cutting-edge ML research and open-source frameworks. Contribute to architectural decisions aligned with clinical, business, and regulatory goals. Data Engineering & Management Process and curate large-scale, structured and unstructured healthcare datasets. Design synthetic data generation strategies where needed to augment training. Handle noisy, imbalanced, or incomplete data through robust preprocessing and enrichment. Ensure HIPAA and GDPR compliance in data handling, encryption, and access management. Leverage EHR and clinical data from the provider side to engineer healthcare data pipelines and training corpora. Bias Mitigation, Evaluation & Governance Lead the creation of robust evaluation strategies combining domain-specific KPIs (e.g., AUC, accuracy) with clinical relevance. Proactively identify, measure, and mitigate algorithmic bias, especially in patient-facing models. Conduct adversarial stress testing and ensure models meet safety, fairness, and ethical standards. Define model KPIs aligned with measurable impacts on clinical outcomes, patient safety, and workflow efficiency. Cross-Functional Collaboration Translate stakeholder and product needs into robust, scalable AI solutions. Collaborate with clinical experts to vet model behavior and expected impact in patient and caregiver workflows. Participate in Agile processes including sprint planning, demos, and technical retrospectives. Manage documentation of model architectures, version changes, testing strategies, and key design decisions. Learning, Innovation & Thought Leadership Stay up-to-date on the latest LLM advancements, STT/TTS developments, voice interaction technologies, and GenAI frameworks. Evaluate new models and methods (e.g., RAG, LangChain, Whisper, Tacotron) for their potential impa