• Model Development: Design, train, and refine machine learning models that tackle real business problems, ensuring they scale effectively in production environments. • Data Pipeline Engineering: Build and maintain robust data ingestion, preprocessing, and transformation pipelines for diverse data sources (structured and unstructured). • AI Workflow Integration: Contribute to end-to-end ML workflows—from serving and monitoring models to evaluating and iterating on their performance. • Advanced AI Techniques: Apply state-of-the-art approaches, including transformers, LLMs, RAG, embeddings, vector databases, predictive modeling, and reinforcement learning, to push the boundaries of what’s possible. • Model Monitoring & Optimization: Support ongoing evaluation and tuning of models to improve accuracy, efficiency, and reliability in production. • MLOps: Help establish best practices for CI/CD, testing, and automated deployment of AI models. • Agile Collaboration: Partner effectively with cross-functional teams in an agile setting, contributing to sprint planning, reviews, and collaborative problem-solving.

• Creating new ways to apply deep learning to solve everyday clinical problems • Image acquisition • Reconstruction • Post-processing and analysis • Designing and implementing algorithms with real-time feedback for the technician and patient • Educating team members with your expertise while also learning from their experience • Contributing to academic publications and attending conferences such as RSNA, ISMRM, SCMR, NeurIPS, CVPR

• Lead engineering of human-in-the-loop digital avatars for behavioral health support • Collaborate with internal teams and stakeholders to deliver machine learning applications • Develop web-based applications and data infrastructures • Mentor and guide junior engineers

• Design and create environments for data scientists to build models and manipulate data • Work within customer systems to extract data and place it within an analytical environment • Learn and understand customer technology environments and systems • Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop • Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models • Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines, and such that the solution can be maintained throughout its life cycle • Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment • Ensure the quality of the delivered product