Engineering Director | "People" first - not "resources" | Drive Large Scale Distributed Platforms | AI/ML - DeepLearning | Data Engineering/Big Data | GCP Architect | Financial Risk | Learner

Engineering Director with 16+ years building AI/ML systems at scale, from startup to Fortune 10. Currently at American Express where I've led digital transformation initiatives generating $5B in revenue and achieved industry-leading 0.8% fraud rates through advanced ML.

I specialize in taking AI from 0->1 - fine-tuning LLMs (RLHF, DPO,ORPO), implementing AI safety frameworks, and deploying edge-optimized models. My recent focus: building trust-worthy AI systems that serve millions while maintaining explainability and safety.

Key Impact:
→ Led $40M+ annual tech investments across 20 global markets
→ Architected systems serving 10M+ users and 10K customer care professionals with 99.9% uptime
→ Pioneered 15-second ML decisioning ($500M revenue stream)

Technical Expertise: LLM fine-tuning, RAG architectures, ML model optimization (quantization, DeepSpeed), distributed systems, Python/Java/Go

I thrive at the intersection of cutting-edge AI and real-world impact. Whether it's making AI safer for vulnerable populations, optimizing models for edge deployment, or building personalization engines from fraud detection systems - I bring both technical depth and strategic vision.

Shailendra Bade

LLMs were never just about language. 🤖

The more I've dived into implementing transformer architectures across different domains, the more one thing becomes clear, Attention is a general-purpose intelligence mechanism, not a language trick.

We built it for words. But it turns out, it's exactly what many other domains needed. I read about Meta GEM in their blog. I feel the same applies to edge computing and IoT devices.

Sensor streams in plants have the same structural problem as a paragraph of text, long-range dependencies, subtle patterns buried in noise, context that only makes sense when you look across the whole sequence, not just the last few steps. 🌱

Traditional ML saw each sensor reading in isolation.
Transformers see the whole story. 🛠️

That's not incremental. That's a category shift in what industrial AI can actually deliver.
After a discussion with someone from the industry last week, I've been exploring how this plays out in IoT analytics, and what it means for real-world deployment at scale, including the edge quantization and compliance requirements that make or break enterprise adoption. 📊

Put my thinking down in a full article. Link below and in comments also 👇

https://lnkd.in/gtxA4gnx

#IndustrialAI #Transformers #LLM #IoT #MachineLearning #GenAI #PredictiveMaintenance

Engineering Director | Gen AI/RAG |AI/ML & LLM Systems at scale | From Financial Fraud Detection to Personalization | Data Engineering & Privacy

Executive with a balanced perspective of business, technology and strategy. 20+ years of strong analytical, organizational, financial and execution skills. Hands-on leader who has worked at every aspect of technology – development, testing, project management to leading large teams specializing in Data & Analytics.  Adept at building and maintaining business relationships while proposing and implementing strategies that improve the bottom line. 

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Shailendra Bade

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The Battle

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Saumya Khanna

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Shailendra Bade's LinkedIn Performance

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