Good morning or good afternoon everyone. My name is Pravin Kumar. I have over 14 years of experience in IT industry, and I currently serve as vice president of engineering in the field of financial technology. Today I'll be talking about the revolution. We are witnessing in platform engineering specifically how AI is reshaping CICD pipelines, developer experience and infrastructure management. We are at a moment where manual reactive processes are giving way to proactive intelligence automation. By the end of this talk, I want you to see. Not only the technical frameworks making this possible, but also the cultural and strategic shifts needed to embrace AI driven platforms. Traditionally, platform engineering was about efficiency and reputability, spinning up environments, managing orchestration, providing stable CICD foundations. But systems today are distributed and they are micro, front and heavy, and globally scaled. The shift we are seeing now is that AI is no longer just a tool for application development. It's actually becoming part of the platform itself with AI platforms. Stop being a reactive s. And becoming proactive collaborators and anticipating needs self-optimizing and continuously improving. Let's be honest, traditional platforms struggle at scale. Too much manual configuration monitoring that tells you what went wrong, but not. What's about to go wrong? Developers spending more time debugging infrastructure than writing features. The real pain point here is complexity beyond human scale. When you have got hundreds of microservices interacting, no human operator or team of them can model those relationships intuitively. That's where. AI brings incredible value by detecting unseen patterns and automating routine pain points. So how AI actually help us think of it as adding predictive and to intelligence layers and do it across the stack. AI observes system data. In real time learns from historic behavior and starts making adjustments proactively scaling before traffic hits, balancing resources before bottleneck occurs, or adapting based on business criticality. This is more than monitoring. It's like a closed loop optimization. Every cycle of data makes the platform smarter. Feeding a S loop of performance and adaptability. So I would like to talk about these architectural patterns, federated AI models. So these federated AI models help keeping decision making distributed close to where the action is. So this helps in avoiding central bottle line. And the next pattern is the AI orchestration layers. These help in serving as the brain of the platform, seeing the ecosystem globally making optimizations, no individual service cut. Coming to even driven intelligence, these help in responding to context in real time. Imagine auto healing triggered not on downtime. But on predictive anomaly detection. And the last pattern is the edge based processing, which help in pushing AI right to the developer of Workflow Edge. Faster code reviews faster, CICD feedback loops. So all these patterns together enable a platform that feels about almost alive in how it reacts and disappear. Let's make this real. In e-commerce, timing is everything. You don't want to lose customers because servers crash on a Black Friday. AI driven infrastructure can autoscale resources preemptive, prioritize critical fixes during peak time, and even handle rollbacks autonomously when a buggy releases. Ships through CICD. That's the difference between downtime, which costs millions per minute and seamless customer experience. Financial systems face a different pain point. Compliance and security. AI driven platforms allow continuous compliance, auditing, automated secure provisioning and predictive risk detection. What that means is not just faster systems, but trustworthy systems. So in finance, intelligence isn't just about efficiency. It's about avoiding catastrophic mistakes. Now, let's talk about the developer. If you are an enterprise team, reducing cognitive load on the developer is good. AI can tailor pipelines, recommend workflow, operations, and even learn from personal coding patterns. It's not about replacing developer decision making, it's about augmenting creativity by removing the friction, the benefit, faster delivery, higher quality, and happier team. Let's talk about the implementation strategies. This transition doesn't happen overnight. The best path is progressive adoption. Start small with intelligent service measures that learn anomaly patterns, and then layer on automated performance tuning applied gradually rather than aggressively. And finally enhance developer workflows with EI assistance. The key is building trust, both in the algorithms and with the engines, engineers who depend on them technical architecture. A good architecture is what prevents AI driven systems from becoming black box. That means careful data pipelines, rigorous model deployment workflows, and the ability to roll back AI models just like application code. Without these guardrails, AI becomes a liability instead of an enabler. Security in AI systems is just, it's not just about infrastructure. It extends to the models themselves. Models are vulnerable to poisoning and virtual effects. So there's a need for regulatory considerations which demand explainability. If your AI engine denies the transaction or reconfigures resources, you must know why. Audit, explainability and strong privacy practices are non-negotiable foundations. It's looking ahead. We are heading towards autonomous platforms environment that adjusts themselves with minimal human touch. Think of it as a self-driving car, but for platforms beyond that, the integration of quantum computing and advanced neural networks promises optimization levels we can't even simulate today. In short, the AI driven platform you adopt now is laying groundwork for a self evolving ecosystem. To sum it up, AI driven platforms represent a fundamental shift. We move from manual firefighting to pro proactive optimization, from static environments to self-learning systems, and from developer burden. To developer empowerment. The organizations that embrace this shift are not just modernizing these tool chains. They are building the foundational autonomous development environments of tomorrow. The future is collaborative where human creativity and AI performance combined to deliver results we couldn't achieve alone. Thank you.