As artificial intelligence systems increasingly influence critical aspects of human life, the demand for their legal accountability, transparency, and jurisdictional defensibility has become urgent. This paper proposes DevLawOps, a novel framework that integrates legal principles directly into the DevOps pipeline to engineer AI systems that are auditable, explainable, and compliant with legal obligations across multiple jurisdictions. Drawing on interdisciplinary literature in law, philosophy, and software engineering, the study develops a layered system architecture that operationalizes legal norms through compliance middleware, jurisdiction-aware modules, and real-time legal databases. The framework reimagines law as a dynamic software component rather than an external constraint, addressing the limitations of existing legal personhood debates and liability models. Through comparative analysis and conceptual modeling, the paper illustrates how DevLawOps anticipates regulatory variation, localizes compliance, and embeds ethical safeguards as executable logic. It argues for a proactive approach to AI governance that makes legal accountability a continuous, automated, and traceable function of system design. The study concludes by offering practical recommendations for implementation and international collaboration, positioning DevLawOps as a forward-looking strategy for governing AI in a fragmented legal world.