.NET Developer (Temporary Position)

About you

You’re a hands-on engineer who thrives on diagnosing complex performance issues and getting systems back to peak efficiency. You take ownership of architecture and optimization, blending analytical depth with practical problem-solving. This engagement is all about tuning, refactoring, and stabilizing, you’ll bring order to legacy code while leveraging AI-driven tools and metrics to surface insights and improvements.

• This is a full-time, temporary engagement (2 months)

You bring to Applaudo the following competencies:

• Bachelor’s Degree in Computer Science, Computer Engineering, or related field (or equivalent experience).
• 8+ years of professional experience in backend development using the .NET ecosystem (C#, .NET Framework, .NET Core).
• Proven expertise in RabbitMQ — message queuing, exchange configurations, performance tuning, and troubleshooting.
• Strong understanding of application performance optimization, including profiling, threading, memory management, and asynchronous processing.
• Experience working with legacy .NET (4.8) applications and modernizing or stabilizing them for scalability and maintainability.
• Familiarity with AI-assisted tools for debugging, code analysis, and performance diagnostics is a strong plus.
• Proficient with SQL Server and database performance tuning.
• Solid grasp of cloud hosting environments (Azure preferred) and containerized deployments.
• English fluency required to collaborate effectively with US-based stakeholders.

You will be accountable for the following responsibilities:

• Conduct a deep technical assessment of the existing .NET/RabbitMQ system to identify architectural and code-level bottlenecks.
• Design and implement performance and reliability improvements across the backend and messaging layers.
• Introduce observability, metrics, and diagnostic tooling to enable data-driven performance tracking.
• Collaborate with client engineering teams to define optimization strategies and measurable success criteria.
• Use AI-powered tools to enhance code analysis, root-cause detection, and remediation processes.
• Document findings, proposed changes, and post-implementation results to support long-term maintainability.