Public discourse surrounding artificial intelligence typically centers on the models themselves—essay-writing chatbots, image-generating platforms, and the competitive race among labs to construct ever-larger systems. Considerably less attention is paid to the infrastructure supporting these technologies under heavy, real-world usage: the automated checks, dashboards, pipelines, and reliability frameworks that determine if an AI application functions properly when accessed simultaneously by millions of users.
Focusing her career on this overlooked sector of the industry, Soumya Trivedi contends that this is precisely where the AI race will be decided. Working as a software engineer at Dell Technologies after previously serving as a technical program manager on AI teams at Meta, Trivedi concentrates on the automation and infrastructure underlying machine-learning products rather than the models themselves.
“Everyone wants to talk about the model. Almost nobody wants to talk about what happens the day after the model ships,” she said. “Reliability, monitoring, the ability to catch a problem before a customer does; that is the unglamorous work that determines whether an AI system is trustworthy or just a demo.”
Her recent projects emphasize this perspective. By creating internal engineering platforms and automating routine checks, Trivedi has achieved tangible operational benefits, including minimized downtime, enhanced reliability, and a reduced manual workload for engineering staff. Such advancements rarely capture media attention, yet they accumulate quietly within large companies, where recurring workflow bottlenecks and preventable outages present genuine financial costs.
According to her, a major opportunity for engineering organizations involves treating internal platforms with the same level of discipline and attention given to products facing external customers. Because the engineers utilizing these platforms function as their users, providing them with superior tools enables the creation of better products for everyone.
“The people using your platform are your engineers, and they deserve the same thoughtfulness a paying customer gets,” she said. “That means investing in clear roadmaps, measuring outcomes instead of just technical output, and asking whether you’ve genuinely made developers more productive. When you improve the developer experience, you’re ultimately improving what the organization delivers to its customers.”
This perspective reflects a blend of product management and engineering, which is intentional. Trivedi’s professional path encompasses both disciplines, transitioning from AI infrastructure program management into hands-on software engineering. She maintains that this dual expertise is vital as AI systems increase in complexity. “The gap between the person who can write the code and the person who can align five teams around why it matters is where a lot of good ideas die,” she said.
Her approach is straightforward and structured: automate repetitive tasks, standardize successful patterns, rely on data rather than opinion to measure outcomes, and utilize shared platforms for scaling instead of manual, one-off processes. She distills this philosophy into a guiding principle often shared with younger engineers: “Automation is not a shortcut, it is a discipline.”
Trivedi remains certain that the industry’s focus is actively moving in this direction. She forecasts that the upcoming phase of AI advancement will arise “not only from larger models, but from better operational systems: the reliability, the governance, the developer productivity that lets teams actually deploy safely.”
