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Video surveillance improves public safety by preventing and sensing criminal activity, enhancing quick counteractions, and presenting evidence to investigators. This is effectively performed by firing a natural language query containing soft biometrics to retrieve a person from a video. State-of-the-art (SOTA) approaches focus on improving retrieval results; thus, the building blocks of any person retrieval system are not accorded due attention, putting novice researchers at a disadvantage. This study aims to provide a design methodology by showcasing the block-by-block construction of a person retrieval system using video and natural language. For each subsystem - natural language processing, person detection, attribute recognition, and ranking- we discuss the available design selections, provide empirical evidence, and discuss bottlenecks and solutions. We thereafter select and integrate the best choices to create an end-to-end system. We highlight the integration challenges and demonstrate that the proposed method achieves an average intersection over union and the true positive rate of <inline-formula> <tex-math notation="LaTeX">$\geq 60\%$ </tex-math></inline-formula>. This is the first study to provide practical guidance to researchers for fast prototyping of person retrieval with subsystem-level understanding and achieve SOTA performance.