AI / ML Intern#

at Applied AI Consulting

During my internship, I worked on building production-grade AI systems, with a strong emphasis on backend reliability, data flow and observability rather than standalone models.

My work involved:

  • Designing and implementing backend APIs to serve ML-driven features
  • Integrating ML pipelines into real-world applications (NLP and vision-based workflows)
  • Handling model inference, preprocessing and postprocessing in production environments
  • Working with asynchronous workflows, background tasks and API-first architectures
  • Ensuring systems were scalable, debuggable and maintainable

I learned to think beyond accuracy metrics, focusing instead on latency, failure modes, logging and reproducibility. A large part of the work was making ML systems behave like reliable software, not experiments.