Hamna B.

Most projects hit the same problems. The backend works but the API is inconsistent. The AI feature looks good in the demo but breaks under real data. The QA process is either missing or done by someone who does not understand the code well enough to find the right bugs. Timelines slip because the person building the feature cannot own it all the way to deployment. These are solvable problems when the engineer on the project has actually dealt with them before. Three years of production work across Django, FastAPI, React, PostgreSQL, Elasticsearch, AWS, Docker, and Kubernetes covers most of what a project needs on the technical side. That includes setting up CI/CD pipelines so deployments are not a manual process, designing APIs that are consistent enough to build on without constant back and forth, and handling large datasets without performance falling apart as they grow. AI integration is its own challenge. The model call is the easy part. The harder part is building the data pipeline around it, validating inputs before they reach the model, and turning the output into something the application can actually use. That work has been done in a live production environment where the outputs informed real operational decisions, not a side project. QA is harder when the tester does not understand how the system was built. When the code is familiar the weak points are obvious. Test cases target the right places, bug reports are specific enough to act on, and API testing with Postman goes beyond the happy path to cover the inputs that actually cause problems. If the project needs someone who can cover the backend, the AI layer, and the QA process without splitting it across three people, send a message. Top 5 Skills • LLM API Integration (OpenAI, Anthropic) • Python and Django Backend Development • Intelligent Automation and Data Pipelines • FastAPI and REST API Development • AWS Deployment with Docker