I’m thrilled to sit down with Dominic Jainy, a seasoned IT professional whose expertise spans artificial intelligence, machine learning, blockchain, and notably, DevOps and Cloud Engineering. With nearly a decade of hands-on experience in transforming tech landscapes across startups to large enterprises, Dominic has navigated the evolving world of DevOps from its early days to the sophisticated practices we see today. In this interview, we dive into his journey, uncovering lessons learned from real-world challenges, the power of proactive planning, the shift to Infrastructure as Code, and the complexities of Kubernetes. Join us as we explore how Dominic turned firefighting into forward-thinking strategies that shape reliable, scalable systems.
How did you first stumble into the world of DevOps, and what was that landscape like almost a decade ago?
Honestly, I kind of fell into DevOps by accident. About ten years ago, I was working as an IT generalist, and a project demanded faster deployments and better collaboration between development and operations. That’s when I started exploring automation and tooling. Back then, the DevOps landscape was pretty raw—CI/CD wasn’t a buzzword in most enterprises, and many teams still relied on manual processes. Kubernetes was this niche thing only a handful of folks dared to touch. It felt like the Wild West, with everyone figuring things out as they went along.
What were some of the biggest shifts you’ve noticed in DevOps practices from those early days to now?
The biggest shift has to be the mainstream adoption of automation and containerization. Back then, setting up a server could take days of manual configuration. Now, with tools like Terraform and Kubernetes, you can spin up entire environments in minutes. CI/CD has also become a standard—most teams wouldn’t dream of deploying without it. Another change is the focus on observability. It’s not enough to just monitor; now we trace requests across services and build systems with failure in mind from the get-go.
Can you walk us through a memorable moment where a deployment almost went wrong, and how you managed to catch it?
Oh, I’ll never forget this one canary deployment early in my career. We were rolling out a new service, and everything seemed fine until I noticed metrics spiking in one environment. Turns out, a staging image was misconfigured and nearly made it to production. Thankfully, the canary setup caught it just in time, and we rolled back before any real damage. It was a wake-up call about how even small oversights can snowball if you’re not watching closely.
How did that close call shape the way you approach deployments moving forward?
It completely changed my mindset. I stopped treating deployments as something that just “should work.” Now, I always assume something could go wrong and plan accordingly. Having solid rollback plans became non-negotiable, and I started integrating monitoring tools like Prometheus and Grafana way before a deployment even happens. They’re like insurance—there to save you when things go sideways.
Speaking of planning for failures, what practical steps do you take to stay ahead of potential issues?
First, I make sure there’s always a rollback strategy—whether it’s a script or a previous image ready to go. I also prioritize testing in environments that mirror production as closely as possible. Pre-deployment checks are critical, like validating configurations and running automated tests. And observability is huge—I set up dashboards and alerts to catch anomalies early. It’s all about building layers of defense so you’re not scrambling when something breaks.
Transitioning to Infrastructure as Code must have been a game-changer for you. What was that shift like?
It was a total turning point. Early on, I was managing infrastructure manually, which was a nightmare—undocumented changes, human errors, you name it. Moving to Infrastructure as Code with tools like Terraform forced us to think through every step. Suddenly, rollbacks were painless, and we could replicate environments consistently. It took some getting used to, but once we got the hang of it, it felt like we’d unlocked a superpower for managing complexity.
How did adopting tools like Terraform impact the way your team collaborated and worked?
It brought a ton of clarity and accountability. Before, changes were a black box—nobody knew who did what. With Terraform, we started treating infrastructure changes like code, with peer reviews and version control. It cut down on miscommunication and made troubleshooting so much easier because everything was documented and traceable. It also sped up onboarding since new team members could just read the code to understand the setup.
Kubernetes is another big piece of the puzzle. What is it about Kubernetes that makes it both powerful and challenging in your view?
Kubernetes is incredible because it gives you unmatched flexibility to scale and manage containerized apps. You can orchestrate complex workloads with ease. But it’s also a beast because that power comes with a steep learning curve and a lot of responsibility. One wrong config can expose vulnerabilities or tank performance. It demands you stay on top of security, networking, and resource management, which can be overwhelming without the right processes in place.
Can you share a specific incident with Kubernetes that taught you a hard lesson, and how it influenced your approach?
Absolutely. Early on, I accidentally exposed a service to the public internet due to a misconfigured service type. It was a simple oversight, but it could’ve been disastrous. That incident made me obsessive about security. We revamped our entire approach to role-based access control and network policies. Now, security audits and strict cluster policies are baked into every setup I touch. It was a harsh reminder that with Kubernetes, you can’t afford to skip the details.
Looking ahead, what’s your forecast for the future of DevOps and Cloud Engineering in the next few years?
I think we’re going to see even tighter integration of AI and automation in DevOps. Tools will get smarter at predicting failures and optimizing resources without much human input. Security will also take center stage as more workloads move to the cloud—expect more zero-trust architectures and policy-as-code adoption. And with the rise of edge computing, I believe managing distributed systems will become a bigger focus. It’s an exciting time, but teams will need to keep learning and adapting to stay ahead of the curve.