Staff Software Engineer (Ruby on Rails)

Here’s a summary of the role:   

As a Staff Ruby on Rails Engineer at Diligent, you will set technical direction for secure, scalable, and high-performing SaaS applications and services that power our governance platform. This role is ideal for you if you thrive on solving the hardest technical problems, shaping architecture across multiple teams, and driving how the organization builds software — including how we responsibly adopt AI into our engineering practices and products. 

You will own critical systems end-to-end, partner closely with Product, Security, DevOps, and other engineering leaders to shape technical roadmaps, and mentor engineers across the department. A key part of this role is helping define our AI strategy: embedding AI responsibly into our systems and workflows, advising on where AI tools and capabilities can meaningfully improve delivery, and raising the bar on how the team uses them safely and effectively. 

Here’s a breakdown of what you’ll do (not all of it, just the important stuff): 

• Champion the design, delivery, and evolution of secure, scalable Ruby on Rails applications and services, driving architecture decisions across multiple teams and codebases, with responsibility for scalability, reliability, and the underlying infrastructure required to run them effectively. 

• Set technical direction for major projects and platform initiatives, from solution design and prototyping through implementation and production ownership. Own and evolve the technical roadmap by identifying, shaping, and driving new technical initiatives and investments. 

• Develop and evolve web applications and services with a strong focus on scalability, maintainability, reliability, and long-term platform health. 

• Identify systemic pain points across services and propose pragmatic architectural improvements, including decomposition of monoliths and evolution toward service-oriented or microservices patterns where it adds value. 

• Partner effectively with Product, QA, Security, and other engineering leaders to shape technical roadmaps, ensure compliance, and deliver working software into production. Help teams move forward by resolving technical ambiguity, identifying cross-team dependencies, and providing clear technical direction where needed. 

• Pioneer and enable the responsible use of AI in engineering workflows — advising on where AI-assisted coding, debugging, prototyping, and other AI capabilities improve speed or quality, and helping teams adopt them with appropriate human validation, monitoring, and governance. 

• Champion code quality through thoughtful code reviews, automated testing, documentation, and continuous improvement of engineering practices across teams. 

• Mentor engineers at all levels, share knowledge broadly, and help raise the bar on technical execution, delivery discipline, architectural thinking, and AI-augmented ways of working. 

• Continuously evaluate and pilot new tools and technologies — including AI toolchains — optimizing for scalability, reliability, performance, cost efficiency, and developer productivity. 

These are the essentials you’ll need to get an interview: 

• 8+ years of professional software engineering experience in a commercial software environment, with deep hands-on experience building and scaling web applications and services. 

• Expert-level understanding of Ruby on Rails and a strong track record delivering production-grade backend or full-stack applications with it at scale. 

• Extensive experience with API and web development, including RESTful service design, and proven ability to design and review scalable, high-performance system architectures, APIs, and data models. 

• Demonstrated experience designing secure software in environments that embrace DevOps, infrastructure as code, and automated delivery practices. 

• Deep understanding of microservices or service-oriented architectures and the trade-offs involved in distributed systems, with experience guiding these decisions across teams. 

• Practical understanding of AI model capabilities, prompt engineering, and responsible/safe AI use, with the ability to advise on and help shape AI strategy and embed monitoring or governance into engineering workflows. 

• Strong experience with source control systems such as Git and GitHub, along with collaborative and branching workflows at scale. 

• Excellent communication skills, with proven ability to influence technical direction, lead cross-functional initiatives, and explain technical and AI-related concepts to both technical and non-technical stakeholders. 

• Proven ability to mentor engineers, set engineering standards, and break down large, ambiguous initiatives into executable work across teams. 

• Strong knowledge of SQL and data modeling, with the ability to reason about application performance, persistence-layer design, and capacity planning at scale. 

• Experience working with AWS-based systems, serverless architectures, and modern cloud deployment and migration patterns. 

#LI-Hybrid