AI Engineer

Opportunity Get Well is seeking an innovative and technically skilled AI Engineer to join our growing team focused on building and deploying cutting-edge AI solutions in healthcare. You will work on developing production-ready AI systems with a deep focus on training and fine-tuning large language models (LLMs)—including unimodal and multimodal models—to solve complex domain-specific problems. This is an exciting opportunity to apply your machine learning and data science skills in a mission-driven environment to help improve precision care, patient engagement, and operational efficiency. You will collaborate across engineering, data, clinical, and product teams, and contribute throughout the entire AI lifecycle—from problem formulation and data engineering to model development and deployment. Responsibilities AI Model Development & Deployment Fine-tune and evaluate LLMs for real-world healthcare 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 and productionalize AI algorithms using healthcare-specific data sources, ensuring integration into clinical and operational products and services. AI Infrastructure & Implementation Write clean, reusable code for model training, evaluation, deployment, and integration. Maintain reproducibility, version control, and experiment tracking using industry best practices. Implement continuous learning workflows using real-time feedback and retraining loops. Build and support voice assistant applications for clinical or patient-facing use cases. Data Engineering for AI Process and curate large-scale, structured and unstructured healthcare datasets. Implement data privacy controls in compliance with HIPAA, GDPR, and internal policies. Design synthetic data generation strategies where needed to augment training. Handle noisy, imbalanced, or incomplete data through robust preprocessing and enrichment. Evaluation, Bias & Governance Perform and validate evaluation benchmarks, and gold standard datasets. Assess and address model bias, drift, explainability, and reliability. Perform unit and integration testing for AI pipelines. Apply responsible AI best practices and contribute to guidance on fairness, ethics, and safety. Collaboration & Agile Development Participate in Agile workflows including daily stand-ups, sprint reviews, and retrospectives. Document design, testing protocols, and implementation details. Learning & Innovation Stay current on advances in GenAI, foundation models, STT/TTS technologies, and multimodal learning. Evaluate emerging tools and frameworks (e.g., Hugging Face, LangChain, OpenAI) for platform integration. Lead or cont...