Code-switching in linguistically diverse, low resource languages is often semantically complex and lacks sophisticated methodologies that can be applied to real-world data for precisely detecting hate speech. In an attempt to bridge this gap, we introduce a three-tier pipeline that employs profanity modeling, deep graph embeddings, and author profiling to retrieve instances of hate speech in Hindi-English code-switched language (Hinglish) on social media platforms like Twitter. Through extensive comparison against several baselines on two real-world datasets, we demonstrate how targeted hate embeddings combined with social network-based features outperform state of the art, both quantitatively and qualitatively. Additionally, we present an expert-in-the-loop algorithm for bias elimination in the proposed model pipeline and study the prevalence and performance impact of the debiasing. Finally, we discuss the computational, practical, ethical, and reproducibility aspects of the deployment of our pipeline across the Web.