Today, we’re thrilled to sit down with Dominic Jainy, an IT professional whose deep expertise in artificial intelligence, machine learning, and blockchain has positioned him as a thought leader in the tech industry. With a passion for integrating cutting-edge technologies into practical applications, Dominic brings a unique perspective on how DevOps practices are evolving to meet modern challenges. In this interview, we dive into the often-overlooked aspects of DevOps, exploring how culture, security, automation, and emerging tech like AI are reshaping the software development lifecycle. From fostering collaboration to securing supply chains and beyond, Dominic shares actionable insights for organizations aiming to stay ahead of the curve.
How do you see the mission of DevOps evolving in today’s tech landscape compared to its early days?
When DevOps first emerged, it was all about breaking down silos between development and operations to speed up releases while keeping systems stable. Today, the mission has grown broader. It’s not just about faster deployments; it’s about aligning with bigger business goals like innovation, customer experience, and even sustainability. With technologies like AI and machine learning becoming integral to software, DevOps now also means ensuring these complex systems are reliable and governed responsibly. It’s a shift from purely technical efficiency to strategic impact.
What approaches have you found effective in building a true sense of shared ownership between dev and ops teams?
Shared ownership goes beyond just having everyone in the same Slack channel. It’s about aligning incentives and metrics. For instance, I’ve seen success when development teams are measured not just on feature delivery but also on production reliability—things like uptime or incident response times. We’ve implemented cross-functional workshops where both teams tackle real production issues together, which builds empathy and trust. It’s also crucial to give developers visibility into operational challenges through shared dashboards or post-mortem reviews, so they feel invested in the system’s health.
How do you integrate security into the DevOps workflow right from the planning stages?
Security can’t be an afterthought—it has to be baked into the process from day one. We start by embedding security requirements into our initial design discussions, ensuring they’re part of the user stories or epics. Tools like static code analysis and vulnerability scanners are integrated into our CI/CD pipelines to catch issues early. We also train developers on secure coding practices regularly, so it’s second nature. The key is making security a shared responsibility, not just a checkpoint at the end, which helps us build software that’s secure by design.
What strategies do you use to manage the risks associated with open-source software in your supply chain?
Open-source software is a double-edged sword—immensely valuable but full of potential pitfalls like outdated packages or licensing issues. We use automated tools to continuously scan our dependencies for known vulnerabilities and compliance risks before they even hit our pipeline. We maintain a curated list of approved components and versions, so developers aren’t just pulling in anything from the wild. Regular audits and visibility dashboards also help our teams stay aware of what’s in use. It’s about striking a balance: leveraging the community’s work while protecting our systems.
Why is standardizing CI/CD pipelines across teams so important, and what benefits have you observed from doing so?
Standardizing CI/CD pipelines eliminates a lot of chaos. When every team has its own setup, you end up with inconsistencies, higher maintenance costs, and more room for errors. By creating a unified pipeline, we’ve cut down on deployment failures and sped up release cycles significantly. It also makes onboarding new team members easier since there’s a consistent process to learn. Plus, it reduces technical debt—fewer custom scripts to debug or outdated tools to replace. It’s a upfront investment that pays off in reliability and efficiency.
How do you apply DevOps principles to areas like database management, which are often overlooked?
Databases are just as critical as application code, yet they’re often treated as separate entities. We apply the same rigor to database schemas by version-controlling them alongside app code, so changes are tracked and tested in sync. We script schema updates and use tools to automate migrations across environments, reducing drift between dev, QA, and production. This approach ensures that when we deploy a new feature, the database is ready to support it without last-minute surprises. It’s about treating data as a first-class citizen in the DevOps workflow.
What’s your take on the role of observability in modern DevOps practices?
Observability isn’t optional—it’s a must-have. Without it, you’re flying blind, especially with complex, distributed systems. We build observability into everything from the start, using standardized instrumentation like OpenTelemetry to monitor logs, metrics, and traces. Integrating these checks into our CI/CD pipelines means we catch issues before they hit production. It’s also about democratizing data; everyone on the team can access dashboards to understand system health. This proactive visibility cuts down on midnight fire drills and keeps our services running smoothly.
How are you seeing DevOps practices expand to include emerging technologies like AI and machine learning?
AI and machine learning are pushing DevOps into new territory. We’re not just deploying code anymore; we’re deploying models, agents, and workflows that need their own kind of monitoring and governance. For instance, we’ve adapted our pipelines to handle model retraining cycles and data pipeline validation. Low-code platforms have also been a game-changer, letting us standardize AI deployments without reinventing the wheel. The focus is on ensuring these systems deliver business value while maintaining reliability, even as the tech itself keeps evolving.
What’s your forecast for the future of DevOps as it continues to intersect with AI and other innovations?
I think DevOps is headed toward even deeper integration with AI, where intelligent automation will handle more of the grunt work—think self-healing systems or predictive incident management. We’ll see DevOps practices become more data-driven, with AI helping optimize pipelines and resource allocation in real time. At the same time, governance will be a bigger focus as regulations around AI and data grow stricter. My forecast is that DevOps will evolve into a more strategic discipline, not just a set of tools or processes, but a core driver of business transformation in a tech-first world.