Financial institutions operate under dual pressures: the need for rapid innovation driven by competition and evolving customer expectations, contrasted with the necessity of adhering to stringent regulatory frameworks like Basel III, CRD V, and SR 11-7. Modern methodologies like DevOps and MLOps promise agility and efficiency but face significant adoption challenges within this regulated context. This paper addresses this critical intersection by consolidating current research on IT architecture, DevOps, and MLOps specifically for the banking sector. We focus on practices supporting robust data aggregation, risk management, and compliance reporting, while acknowledging persistent challenges such as legacy system integration and rigorous model governance. Recognizing a gap between general principles and practical implementation guidance, we propose two concise, research-grounded architectural blueprints. These blueprints offer actionable models for designing integrated DevOps/MLOps workflows that ensure continuous compliance and operational resilience, providing valuable insights for practitioners and researchers navigating the complex interplay of agile development and financial regulation. INTRODUCTION The global financial services industry exists in a state of continuous flux, driven by intense market competition, shifting customer demands for digital services, and an ever-more complex web of regulations (48; 2). International accords like Basel III (9), regional directives such as CRD V (16), and national guidance on critical areas like model risk management (e.g., the US Federal Reserve's SR 11-7 (11)) impose strict operational and reporting requirements. Consequently, financial institutions must constantly evolve their Information Technology (IT) architectures and operational processes to simultaneously achieve agility, maintain resilience, and ensure unwavering regulatory compliance (18). This balancing act represents a central challenge for the sector. Modern software engineering and operational paradigms, notably DevOps (26; 21) and Machine Learning Operations (MLOps) (46; 13), offer significant potential benefits. DevOps practices aim to break down silos between development and operations, automating delivery pipelines to increase speed and reliability. MLOps extends these principles to the unique lifecycle of machine learning models, addressing challenges like reproducibility, monitoring, and governance crucial for financial applications from fraud detection to algorithmic trading. The state-of-the-art involves highly automated CI/CD pipelines, infrastructure managed as code, and increasingly sophisticated model management platforms. However, the adoption of these modern practices within the highly regulated financial context is far from straightforward (15; 2). The core tenets of DevOps and MLOps-speed, iteration, and continuous change-must be carefully reconciled with non-negotiable regulatory demands for security, auditability, data integrity, robust governance, and transparent reporting. Furthermore, many institutions grapple with significant legacy systems, which often represent substantial technical debt and hinder modernization efforts (31). While research explores DevOps (43) and MLOps (6) adoption challenges, and the potential of RegTech (8), a gap often exists between high-level principles and concrete architectural guidance tailored for financial compliance. This paper aims to bridge this gap by providing actionable architectural blueprints. We synthesize current academic knowledge and industry best practices concerning IT architecture, DevOps, MLOps, and regulatory compliance within finance. Our contribution lies in presenting two distinct, yet principled, reference models (Section 3) designed to address common scenarios: modernizing domestic institutions with legacy cores, and managing complex international operations under multiple regulatory regimes. These blueprints provide concrete structures for integrating DevOps and MLOps workflows in a manner that fosters continuous compliance alongside operational excellence, offering practical value to practitioners and a structured basis for further academic inquiry. The subsequent sections review relevant background literature (Section 2), detail the proposed blueprints (Section 3), discuss their implications (Section 4), and offer concluding remarks (Section 5). BACKGROUND