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Nirmal Jingar: How to Ship Enterprise AI to Production at Enterprise Scale

Nirmal Jingar: How to Ship Enterprise AI to Production at Enterprise Scale

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Nirmal Jingar is a Senior Technology Leader specializing in artificial intelligence, enterprise platforms, and large-scale supply chain systems. He leads engineering organizations responsible for AI-driven decision platforms supporting one of the world's largest e-commerce supply chains, and has delivered hundreds of millions of dollars in cumulative business impact through AI, optimization, and platform modernization initiatives. In this PreVetted Podcast episode, Nirmal breaks down what it actually takes to ship enterprise AI to production: designing supply chain systems for enterprise scale from day one, and building the ownership culture and guardrails that keep AI reliable in live, high-volume operations.

Federico Ramallo and Nirmal dig into why forecasting against real order volume is the hard problem behind supply chain scale. When a Black Friday spike jumps from a planned 10,000 orders per minute to 50,000, everything downstream (inventory, warehouse staffing, carriers like FedEx and USPS) has to move. His advice to engineers: architect for 10x to 100x load from the start instead of optimizing a prototype.

They get concrete on how AI is changing enterprise software development: AI now generates the majority of first-draft code across many engineering teams, fundamentally shifting the role of software engineers toward system design, review, governance, and decision making, and a review playbook Nirmal generated by having AI scan three years of pull-request comments across a team's repositories. He explains why AI-ready PRDs and tech specs, a deep wiki of architecture and history, and clear system and domain boundaries are what let large enterprises, not just greenfield startups, get real value from agentic coding.

The conversation also covers leading distributed engineering teams across time zones (async-first communication, decision logs, extreme ownership), the enduring value of long-tenured subject-matter experts that AI cannot replace, and Nirmal's patented agent-based system for supply chain incident detection and root cause analysis. His throughline: AI agents should reduce the noise for humans, not remove human accountability.

About Nirmal Jingar
Sr. Technology Leader in Artificial Intelligence, Enterprise Platforms, and Supply Chain Systems.
- 💼 https://www.linkedin.com/in/nirmaljingar/
- 🎤 TEDx: https://www.youtube.com/watch?v=pxUChqiyp2Y

About Federico Ramallo
Founder of Density Labs, the AI Engineering Partner for the mid-market, helping US companies ship AI to production, backed by a decade of shipping software with distributed engineering teams. Author of The Invisible Distance, the operating system for engineering leaders running teams across borders. Host of The PreVetted Podcast: 150+ conversations with VPs of Engineering, CTOs, and operators on what actually works.
- 💼 https://www.linkedin.com/in/framallo/
- 🌐 https://densitylabs.io
- ✅ https://prevetted.ai

🎙 PreVetted Podcast
- 🎯 https://prevetted.ai/podcast
- 🐦 https://x.com/PrevettedPod
- 🔗 https://www.linkedin.com/company/prevetted-podcast

TIMESTAMPS:
0:00 Introduction: shipping enterprise AI to production
2:50 The complexity of supply chain systems at scale
5:37 Where AI helps most in supply chain optimization
8:09 Human expertise vs. AI in engineering judgment
10:50 Designing supply chain systems for enterprise scale
13:34 Building a culture of ownership and accountability
16:05 Proactive communication in distributed teams
19:03 The human side of technology and team dynamics
21:35 Collaborating across time zones with async-first practices
23:51 Staying technically deep as an executive leader
27:24 How AI changed enterprise software development
33:55 AI-ready PRDs, tech specs, and a deep wiki for context
42:55 The future of engineering in an AI-driven world
45:33 Patented AI agents for supply chain incident detection

#EnterpriseAI #SupplyChainAI #EngineeringLeadership
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