This paper provides an empirical and conceptual account on seeing machine learning models as part of a sociotechnical system to identify relevant vulnerabilities emerging in the context of use. As ML is increasingly adopted in socially sensitive and safety-critical domains, many ML applications end up not delivering on their promises, and contributing to new forms of algorithmic harm. There is still a lack of empirical insights as well as conceptual tools and frameworks to properly understand and design for the impact of ML models in their sociotechnical context. In this paper, we follow a design science research approach to work towards such insights and tools. We center our study in the financial industry, where we first empirically map recently emerging MLOps practices to govern ML applications, and corroborate our insights with recent literature. We then perform an integrative literature research to identify a long list of vulnerabilities that emerge in the sociotechnical context of ML applications, and we theorize these along eight dimensions. We then perform semi-structured interviews in two real-world use cases and across a broad set of relevant actors and organizations, to validate the conceptual dimensions and identify challenges to address sociotechnical vulnerabilities in the design and governance of ML-based systems. The paper proposes a set of guidelines to proactively and integrally address both the dimensions of sociotechnical vulnerability, as well as the challenges identified in the empirical use case research, in the organization of MLOps practices.