AI has made cloud the default place where models are trained, served, observed, and governed—so teams that can run GenAI on cloud stacks (data + GPUs + LLMOps) are in highest demand and deliver outsized business impact. What’s driving demand The new core stack Why cloud beats on‑prem for most AI Emerging patterns to know … Read more
Hiring is shifting to skills-first portfolios where AI, data, and cloud meet governance—so students who can build, deploy, and safely operate AI systems will see the strongest demand. Roles on the rise Skills to prioritize in 2026 India outlook Portfolio that gets interviews How to learn efficiently 30‑day study plan Bottom line: focus on building … Read more
IT engineers already have most of the muscle needed—systems thinking, debugging, cloud, CI/CD—so the fastest path is adding applied AI building blocks (LLMs, RAG, agents) plus LLMOps, then proving it with deployed artifacts and metrics. Pick your target lane Bridge skills from IT to AI Minimal stack to learn in 2026 Portfolio that gets callbacks … Read more
Mastering these technologies in 2025–26 will future‑proof careers as AI shifts from single models to reliable, efficient, and integrated systems spanning cloud to edge. How to learn fast (30‑day plan) Bottom line: focus on agentic, multimodal, retrieval‑rich, and production‑ready AI across cloud‑to‑edge—with privacy, safety, and networking baked in—to stay relevant as systems become more autonomous … Read more
The most important innovations are those turning prototypes into reliable products: grounded LLMs, tool‑using agents, multimodal/embodied models, and the ops stacks that make them safe, fast, and cheap at scale. How to upskill fast (6 projects) Bottom line: prioritize agent frameworks, multimodal/embodied models, advanced retrieval, mature LLMOps, robust evaluation, and secure, efficient deployment—this is the … Read more