Sahil Yadav
Chief Product Officer, TelemeTrak | Former Cisco, GE, IBM
San Francisco, California, United States
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Sahil Yadav is Chief Product Officer at TelemeTrak, where he leads AI-powered infrastructure platforms for government and industrial clients. He has previously launched AI based network automation platforms at Cisco, AI driven predictive analytics products at GE, and industrial AI safety systems at Guardhat, each used by Fortune 500 firms and mission-critical operations. With 13+ years of experience in AI product management, he specializes in scaling edge/cloud AI, driving enterprise adoption. His expertise lies in building resilient, AI-driven systems that work where others break, behind firewalls, across compliance borders, and in zero-trust environments.
Area of Expertise
Topics
How to Build AI Products When Half the Stack Is Behind the Customer's Firewall
Building AI products is hard—but building them when half of the architecture lives behind a customer’s firewall is a different game altogether. In this 18-minute talk, I’ll unpack lessons from designing and scaling hybrid AI systems in heavily regulated, edge-driven industries like telecom, manufacturing, and public safety.
I’ll cover:
Design patterns for AI in hybrid environments (on-prem + cloud)
Trade-offs in data access, latency, and model explainability
Privacy-aware ML pipelines, RBAC enforcement, and air-gapped fallback strategies
Real-world examples of deploying AI in zero-trust, high-compliance environments
This talk is ideal for AI architects, infra engineers, and product leaders navigating the challenges of bringing modern AI to legacy, siloed systems without compromising trust or performance.
How to Build AI Products When Half the Stack Is Behind the Customer's Firewall
Building AI products is hard, but building them when half of the architecture lives behind a customer’s firewall is a different game altogether. In this talk, I’ll unpack lessons from designing and scaling hybrid AI systems in heavily regulated, edge-driven industries like telecom, manufacturing, and public safety.
I’ll cover:
- Design patterns for AI in hybrid environments (on-prem + cloud)
- Trade-offs in data access, latency, and model explainability
- Privacy-aware ML pipelines, RBAC enforcement, and air-gapped fallback strategies
- Real-world examples of deploying AI in zero-trust, high-compliance environments
This talk is ideal for AI architects, infra engineers, and product leaders navigating the challenges of bringing modern AI to legacy, siloed systems without compromising trust or performance.
CIOs and Industry Leaders: Do You Trust Your AI’s Inferences?
Enterprise AI adoption is accelerating, but with it comes a hard question: Do we trust the model’s decisions? In this 18-minute talk, I’ll explore the invisible risks behind automated decision-making in safety-critical and revenue-sensitive environments. Drawing on case studies across manufacturing, telecom, and industrial IoT, I’ll highlight how explainability, traceability, and robust guardrails drive adoption and protect enterprise value.
Attendees will walk away with:
• A 3-step framework for operationalizing AI trust
• Real-world lessons from building guardrails in on-prem and hybrid systems
• Tools and techniques for debugging and explaining inferences at scale
• A blueprint for building trust between models, engineers, and executive stakeholders
CIOs and Industry Leaders: Do You Trust Your AI’s Inferences?
Enterprise AI adoption is accelerating—but with it comes a hard question: Do we trust the model’s decisions? In this 18-minute talk, I’ll explore the invisible risks behind automated decision-making in safety-critical and revenue-sensitive environments. Drawing on case studies across manufacturing, telecom, and industrial IoT, I’ll highlight how explainability, traceability, and robust guardrails drive adoption and protect enterprise value.
Attendees will walk away with:
A 3-step framework for operationalizing AI trust
Real-world lessons from building guardrails in on-prem and hybrid systems
Tools and techniques for debugging and explaining inferences at scale
A blueprint for building trust between models, engineers, and executive stakeholders
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