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

An accomplished IT professional with demonstrated history of providing consulting services to the clients (spanning diverse industries and world class brands) in the area of digital transformation, business and technical architecture,  enterprise application design, development, change management, scalability and performance improvement.

Hands-on experience in designing and implementing innovative business solutions using multiple Pega versions( 5.x, 6.x, 7.x, 8.x) and Pega frameworks/products like SI for payments, Customer Service(CSI, CSHC, CSFS), Sales Automation, HCIF, Pega Survey, BIX, Pega Robotics, Pega Decisioning and Pega Marketing.

Performed various key roles with responsibilities including:
Leading the architecture, design, development, performance and scalability for complex Pega applications and platforms.
Providing strategic thinking, technical advice and guidance to project teams/stakeholders, establishing standards, sizing projects, managing scope and risk, conducting elaboration sessions and facilitating coordination among teams (vendors, application owners and software engineers) during various projects involving geographically distributed teams.


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