About the role
Here’s a summary of
the role: As a Staff Software Engineer at Diligent, you will play a key technical leadership role in building the product engineering capabilities behind our governance platform. You will design and deliver secure, scalable, and resilient cloud-native services that turn data, insights, and actions into meaningful customer value. This is a hands-on role for an experienced engineer who is comfortable operating at both system and organizational level. You will shape architecture, influence engineering direction across teams, and stay close enough to the implementation to help solve complex technical problems. You will work closely with Product, Design, Security, DevOps, and peer engineering leaders to evolve our platform, improve reliability and developer experience, and build solutions that scale. A key part of this role is helping us adopt AI thoughtfully and responsibly. You will influence how AI capabilities are introduced into our products and engineering workflows, guide technical decisions around model usage and prompt design, and help ensure that governance, observability, and safety are built into AI-enabled systems from the start. Here’s a breakdown of what you’ll do (not all of it, just the important stuff): Lead the design and delivery of secure, scalable, high-performance services and platform capabilities, making sound architecture decisions that balance speed, resilience, maintainability, and cost. Set technical direction across multiple teams, identifying system pain points, driving platform and architectural improvements, and helping shape the long-term evolution of our product engineering landscape. Own complex initiatives end-to-end, from problem framing and technical design through implementation, deployment, monitoring, and iteration, ensuring high quality and operational excellence throughout. Partner closely with Product, Design, Security, DevOps, and other engineering leaders to shape roadmaps, align technical strategy with business goals, and deliver compliant, high-impact customer outcomes. Drive pragmatic AI adoption across product and engineering, advising on model capabilities, prompt strategy, system design, and embedded governance, monitoring, and safety practices. Mentor engineers and raise the technical bar through strong engineering practices, thoughtful feedback, architectural leadership, and a culture of learning, experimentation, and accountability. These are the essentials you’ll need to get an interview: 7+ years of experience building and operating secure, scalable software systems in agile commercial environments, with the judgment and technical depth expected of a staff-level engineer. Strong hands-on experience designing distributed systems, APIs, microservices, and data models, with the ability to write high-quality, testable, maintainable code in modern engineering stacks such as .Net and TypeScript/JavaScript. Proven experience shaping technical direction across teams, leading complex engineering initiatives, and influencing architecture and engineering practices beyond a single team. Strong cloud-native engineering experience, ideally on AWS, including modern deployment patterns, infrastructure automation, observability, operational ownership, and cost-aware system design. Solid DevOps and platform engineering capability, including CI/CD, infrastructure-as-code, production monitoring, incident response, and resilient service operation. Practical experience integrating security best practices into software delivery, including identity and access controls, secrets management, privacy considerations, and secure-by-design engineering approaches. Demonstrated ability to evaluate, design, and deliver AI-enabled solutions in production, including understanding of model capabilities and limitations, prompt design, and the governance, monitoring, and safety considerations required for responsible adoption. Strong communication and stakeholder management skills, with the ability to explain complex technical and AI-related concepts clearly, build alignment across functions, and mentor engineers effectively in AI-augmented workflows. It would be great if you had these to, but we’ll support you if you don’t: Experience in product engineering environments where customer-facing workflows, user actions, and domain-specific platform capabilities are central to product value. Exposure to data-intensive systems, including data pipelines, reporting, analytics, or event-driven architectures. Familiarity with front-end technologies such as Angular or React, where useful for influencing end-to-end solution design.