Modern AI Engineering: Strategies That Scale
As AI moves from pilot to production, you need a clear engineering roadmap to scale reliably. In this session, we’ll cover Generative AI essentials and the Agentic AI landscape, then dive into the five pillars of enterprise-scale AI engineering: Business Alignment, Infrastructure Design, Data Architecture, Inference Optimization and Regulatory Compliance.
This webinar gives you a hands-on playbook to turn AI into measurable business impact, build systems that serve millions, streamline data workflows for continuous iteration, speed up inference, and bake in compliance controls from day one.
💡 What you’ll learn
Align AI to business KPIs with right evaluations
Scale infrastructure and data pipelines
Optimize inference cost and speed, and
Security & compliance from day one
About the Speakers
Sandeep Kaipu is a software engineering leader with over a decade of experience driving innovation in mobile, cloud, and AI technologies. As Software Engineering Manager of R&D at Broadcom, he has led the development of cloud computing and AI infrastructure platforms. His career includes senior engineering roles at VMware, Nokia, and Samsung Electronics, where he delivered key technological advancements and secured 10 issued patents. Beyond his hands-on work, he contributes to the tech community as an author, speaker, and advisor and is recognized as a thought leader in software systems engineering.
Nikhil Pareek, Founder & CEO of Future AGI, is a serial entrepreneur with over a decade of experience in building startups and leading AI-driven innovation across industries like healthcare, IoT, consulting, finance, etc. He’s passionate about bringing engineering rigor to AI systems and solving core infrastructure challenges in model development, evaluation and optimization. At Future AGI, he is committed to revolutionize the AI development workflow by empowering tech and non-tech teams to collaborate and ship trustworthy AI, 10x faster.
About Future AGI
Future AGI is a San Francisco-based advanced AI Lifecycle Management platform designed to streamline experimentation, evaluation, and real-time observability. Traditional AI tools often rely on guesswork due to gaps in data generation, error analysis, and feedback loops. Future AGI eliminates this uncertainty by automating the data layer with multi-modal evaluations, agent optimisation, observability, and synthetic data tools, cutting AI development time by up to 95%. By removing manual overhead, it brings software engineering rigor to AI, enabling teams to build high-performing, trustworthy systems faster.
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