What will you be responsible for? • Design, develop, test, and maintain secure, scalable full-stack applications, APIs, integrations, and cloud-native services. • Deliver well-tested features across front-end, backend, database, and integration layers using modern engineering practices. • Apply DevOps, CI/CD, source control, automated testing, monitoring, and deployment practices to improve software quality and reliability. • Collaborate with product owners, architects, senior engineers, data teams, and operations partners to understand requirements and deliver business value. • Contribute to continuous improvement, secure coding, operational excellence, and responsible use of AI-enabled developer productivity tools. • Build MLOps and Agentic scenarios for the platform. What would your day look like? On a typical day, you will build and enhance full-stack features, develop reusable backend services and APIs, create responsive user experiences, write automated tests, troubleshoot issues, support deployments, and collaborate with cross-functional teams to deliver reliable cloud and on-premises solutions that support Providence’s mission of Health for a Better World. Who are we looking for? • B.Tech. or BS in Computer Science, Engineering, or a related field, or equivalent practical experience, with 6–9 years of professional software engineering experience. • Strong hands-on experience with Microsoft .NET technologies such as C# and .NET Framework/.NET Core, or comparable backend programming experience. • Front-end development experience with React, Angular, HTML, CSS, JavaScript, and/or TypeScript. • Experience with SQL databases, relational data modeling, APIs, microservices, source control, unit testing, debugging, and secure coding practices. • Working knowledge of cloud-native development, Azure or similar cloud platforms, CI/CD pipelines, Docker/Kubernetes exposure, and Agile/Scrum delivery using Azure DevOps and Git. • Strong problem-solving, communication, collaboration, ownership, learning agility, and ability to work effectively with ambiguity in a fast-paced engineering environment. • Hands-on experience on building ML and AI Ops scenarios.