AI Systems Architect & Technology Leader
Writing about AI infrastructure, edge intelligence, and the engineering realities that hype cycles miss. 20+ years building the systems AI actually runs on.
Chhavi Jain is an AI systems architect and technology leader with 20+ years building large-scale telecommunications and machine learning platforms. She has led enterprise AI platform initiatives across financial services and technology, spent a decade at Qualcomm driving AI inference optimization and on-device ML powering billions of devices — partnering with Apple, Meta, Google, and Samsung — and founded Live AI Dream, a GenAI inference platform company built in partnership with Microsoft Azure and NVIDIA.
She holds an MS in Data Science & AI from UIUC, an MBA from IIM Calcutta, and is a recipient of the President of India Award, and a TinyML certification from Harvard. She serves as IEEE SPS Vice Chair and mentors at UCSD at the intersection of AI research and real-world impact.
Her technical work spans LLMs, RAG, agentic AI, quantization, and distributed systems. She has published IEEE research, holds multiple granted patents in 5G + AI systems, and has spoken at TinyML, IEEE Women in Engineering, and industry AI conferences globally.
Published on Substack, Medium, and LinkedIn. Writing from 20 years inside the infrastructure AI depends on.
Buying AI licenses and hoping for 10x productivity is like buying gym equipment and expecting to get fit. The equipment isn't the hard part.
After 20 years building AI systems across Nokia, Intel, and Qualcomm — and leading enterprise AI transformation at scale — I built the AgentReady™ Assessment to measure the six things that actually determine whether agents deliver real value for your team.
"The teams getting 10x from agents aren't the ones with the best tools. They're the ones who redesigned how they work."
— Chhavi Jain
30 questions · ~15 minutes · No signup required
Can an agent navigate, understand, and modify your codebase without getting lost? Documentation, modularity, test coverage, and review processes all matter here.
Are your processes documented as specs an agent can follow — or locked in people's heads? The gap between "tribal knowledge" and "executable spec" is where most teams stall.
Can you automatically verify that AI-generated output is correct, secure, and compliant? Without verification, you can't trust agent output at scale.
Can your engineers write effective specs, evaluate agent output, and debug agent failures? Adopting agents without developing these skills produces impressive demos, not production systems.
Are AI tools integrated across your pipeline and governed properly — or scattered across individual laptops? Integration depth and measurement rigor separate 2x teams from 10x teams.
Does leadership understand this is a paradigm shift, not a tooling upgrade? Teams where leadership doesn't grasp the difference consistently under-invest in the things that actually matter.
Free · 30 questions · No account needed · Save progress anytime
Speaking at the intersection of on-device AI, TinyML, wireless intelligence, and engineering leadership.
The engineering problems that don't make headlines — but determine whether AI works at scale.
Notable open-source contributions from my decade at Qualcomm — tools used by researchers and engineers worldwide to optimize AI models for deployment on edge devices.
Advanced quantization and compression library for trained neural network models. AIMET enables INT8 and INT4 quantization with less than 1% accuracy loss — making large models deployable on edge devices like smartphones without retraining. Techniques include AdaRound, SeqMSE, Cross-Layer Equalization, and Quantization-Aware Training for PyTorch and ONNX models.
3 granted patent families in 5G + AI systems. 5 provisional families in Edge AI. Filed across the United States, Europe, China, and PCT.
Patent citations from industry leaders across telecommunications and device manufacturing — reflecting the influence of these innovations on later generations of wireless and AI systems.
Cited by
Media inquiries, speaking engagements, collaboration, or a conversation about edge AI and the infrastructure layer.