Kristian Wohinzha focused on backend reliability and optimization across AI-Hypercomputer repositories, addressing critical issues in model conversion and resource allocation. In maxtext, Kristian resolved parameter mapping inconsistencies in the Gemma3 model checkpoint conversion and mitigated out-of-memory errors in the to_huggingface pipeline by reconfiguring JAX and TensorFlow for memory-constrained environments. For the xpk repository, Kristian stabilized DWS Calendar reservation flows by correcting accelerator type identification, ensuring accurate resource matching and reducing allocation failures. Throughout the two-month period, Kristian applied expertise in Python, data processing, and model optimization, delivering targeted bug fixes that improved workflow stability and deployment efficiency.