Dynamic Pricing for online retail has been discussed extensively in literature. However, past solutions fell short of addressing the unique challenges of independent multi-retailer platforms for grocery delivery. From limited visibility of retailers' inventories to diverse demand-side dynamics across retail brands and locations, the highly decentralized nature of multi-retailer platforms deviates from the classical framework of modeling price elasticity and cross-elasticity of demand. In this paper, we present a novel scheme to scalable and practical price adjustment in the highly dynamic multi-retailer context. First, we present a deep learning framework to distinctly model complex cross-elasticity relationships via additive neural networks augmented with adversarial data. Second, we present evolutionary optimization agents for adjusting itemized prices in a location-decentralized manner, while adhering to custom business constraints and objectives. The optimization utilizes the genetic algorithm structure, where we introduce a potential mechanism, inspired by bandit algorithms, in order to improve convergence speed by managing exploitation and exploration trade-offs. Our solution is deployed at Shipt and is extendable to other types of multi-retailer platforms, such as restaurant delivery. Finally, we empirically demonstrate performance using public and industry datasets of hundreds and thousands of diverse products across tens of stores, offering an optimization targets coverage scale in the tens of thousands, far larger than experimental setups in past research.