Tensor Contraction (TC) is the operation that connects tensors in a Tensor Network (TN). Many scientific applications rely on efficient algorithms for the contraction of large tensors. In this thesis, we aim to develop a transposition-free TC algorithm for complex tensors. Our algorithm fuses high-performance General Matrix-Matrix Multiplication (GEMM), the 1M method for achieving complex with real-valued GEMM, and the Block-Scatter layout for tensors. Consequently, we give an elaborate overview of each. A benchmark for a series of contractions shows that our implementation can compete with the performance of state-of-the-art TC libraries.