• Design, build, and deploy production-ready machine learning models that power critical features across our platform • Collaborate with your team to transform research prototypes into robust, scalable, and maintainable ML systems • Work closely with product and engineering teams to understand business problems and define technical ML solutions • Improve recommendation algorithms that personalize user experiences and drive engagement • Develop and maintain ML pipelines that can reliably process large volumes of data (LLM) • Monitor model performance in production and implement strategies to detect and address model drift • Participate in code reviews and mentor junior engineers in ML best practices • Stay current with the latest advancements and evaluate new techniques for potential application • Work on cutting edge projects that are pivotal to our Fortune 500 client

• Work closely with Machine Learning Engineers to understand, refine, and prioritize requirements • Design and build our model serving service with simple, powerful APIs to capture our users/clients needs • Build our core ML model lifecycle management system to provide an ML-aware release and deployment experience • Improve machine learning data quality by using & building tools to automatically detect issues • Create intelligent ML-aware real-time monitoring & observability systems • Work closely with partner teams to integrate with other ML tools to create a seamless end-to-end experience • Leverage open-source technologies like Kubeflow, Kubernetes, Spark, Docker, Airflow, Tensorflow, and PyTorch

• Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git). • Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use. • Implement basic monitoring tools to track model performance, data drift, and system health in production. • Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference. • Help build and maintain feature stores and data layers that ensure consistency between training and production environments. • Work closely with backend teams to integrate ML outputs into our core healthcare applications. • Write clean, maintainable, and well-documented Python code. • Participate in code reviews to ensure system reliability. • Use Docker and Kubernetes to package and orchestrate ML workloads across different environments. • Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards.

• Work on Wayfinding projects that impact the business and contribute to Transcarent’s mission of better health and care • You'll gain exposure to how a real engineering team works, from sprint planning to shipping code • You’ll be exposed to experienced engineers from all backgrounds (web development, mobile, API, Salesforce, etc.) • You'll have a formal mentor on your team guiding you throughout your internship period