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.
