The Hidden Innovations Powering Today’s Cloud Infrastructure

In an era where artificial intelligence is reshaping everything from healthcare to finance, the attention often rests on the outputs: faster predictions, sharper models, more personalized experiences. But what’s less visible is the foundation on which these systems are built. It is here, in the backend layers of DevOps and infrastructure, that some of the most critical challenges and innovations are unfolding. A recent BCG report found that 74% of companies struggle with scaling and achieving value. One of the most significant hurdles? Ensuring the underlying infrastructure can support continuous iteration without compromising reliability or security. Downtime and silent failures aren’t just annoyances, they can derail entire product pipelines and result in reputational damage. This is especially true in hyperscale environments, where workloads run across thousands of machines and where a seemingly minor disruption can cascade into a broader system failure. One of the most delicate processes in this world is live migration, the act of moving running applications between physical hosts without interrupting services. It is high-risk, high-reward territory, especially when applied to custom hardware like AI accelerators. Within this niche, Chhaya Gunawat has been quietly changing the equation. A systems engineer and architect specializing in DevOps for AI infrastructure, Chhaya led the development of an automated framework for testing and deployment on custom silicon platforms. An effort that has drawn interest for both its technical sophistication and its practical impact. At a leading global cloud services provider, where proprietary chips now power a growing share of AI workloads, the stakes are high. Firmware must be validated with precision, and any disruption in live migration could risk customer-facing downtime. Chhaya’s framework established a fully automated pipeline that not only extracts firmware binaries and applies critical security protocols but also runs pre-deployment validations to catch faults before they propagate. More than a test suite, the system is built for resilience. It filters out faulty builds automatically, ensuring that only production-ready firmware makes it through. What used to require days of manual oversight now happens in hours or less, without sacrificing quality. “The goal wasn’t to just remove human intervention,” she said in a recent interview. “It was to make the system smart enough to learn from failure and adapt in real time.” The implications were immediate. Teams that once responded to failures after the fact were now working in a preventive mode, addressing potential disruptions before they reached production. Within months, her approach was expanded to new hardware features using the same framework with minimal adaptation. Chhaya was soon asked to drive AI adoptions with scaling the solution across more platforms. Colleagues and observers say her work represents a shift in how infrastructure is managed away from reactive incident response, toward intelligent automation and early intervention. Beyond automation, Chhaya embedded observability and diagnostics deep into the pipeline. Dashboards now publish live performance metrics, and failures generate tickets automatically. More notably, she introduced a machine learning–based log analysis automation, which sifts through massive datasets to identify anomalies and triage them without human oversight. That advance alone has helped avoid multiple deployment rollbacks. The work earned Chhaya recognition internally and externally. Her research on AI-Augmented DevOps and causal inference-based root cause analysis has been cited in respected journals and IEEE conferences. One of her frameworks for cloud disruption resolution has not only been published but also implemented in active production environments, an uncommon feat for academic contributions. But her work extends beyond the technical. As a speaker and advocate, Chhaya has consistently used her platform to promote diversity in engineering. Her episode on the Tech.Strong.Women podcast, titled “Breaking Barriers in DevOps,” has resonated across the industry. In it, she talks not just about her systems, but about the importance of building spaces where innovation and inclusion go hand-in-hand. That alignment is visible in her day-to-day work. She has mentored returning engineers through technical returnship programs and helped establish internal self-service tools that reduce onboarding friction. One such chatbot, which contextualizes diagnostic data for quicker support resolutions, is now being deployed across other business units. “Reliability used to mean making sure the lights stay on,” she said. “Today, it means predicting which bulb is likely to fail, and replacing it before it happens.” Chhaya’s thinking is resonating at a time when many companies are re-evaluating their infrastructure in light of AI’s growing demands. It is no longer enough to have fast hardware. What matters is whether that hardware can be deployed, maintained, and scaled with confidence. Her frameworks offer a roadmap: intelligent automation, deep observability, and a relentless focus on resilience. Her work is not flashy. It doesn’t chase headlines or hinge on dramatic breakthroughs. Instead, it reflects a kind of quiet, systems-level innovation, hard to see, but essential to everything that follows. As the AI boom continues, engineers like Chhaya Gunawat are not just helping systems run more smoothly. They’re changing how the industry defines responsibility, resilience, and readiness.