Traditional methods for cosmological parameter inference from Large Scale Structure (LSS) rely on summary statistics, such as power spectra, which may not fully capture the complex non-linear and non-gaussian features of the LSS. SBI, which uses forward models of the observables and machine learning to learn a posterior distribution over the parameters, can provide more robust inferences. This work presents novel constraints using SBI on LSS at field-level using Convolutional Neural Networks (CNNs) and Bayesian Neural Networks. We use the SimBIG forward modeling pipeline to generate realistic mock observations of the Baryon Oscillation Spectroscopic Survey (BOSS) at different cosmologies. We show that our method provides tighter constraints on cosmological parameters than methods based on compressing the data to the power spectrum, likely due to the CNN's ability to exploit non-Gaussin information. Furthermore, we validate our pipeline on out-of-distribution data generated using different forward models and show that our constraints generalize well, providing some robustness against model misspecification. This paper not only presents field-level parameter constraints from real LSS observations, but also introduces methods that will be useful for future analyses on larger boxes and smaller scales with SDSS data and future surveys like DESI.