In conversation with Neeraj Poddar, NimbleEdge's new VP of Engineering

Neeraj Poddar
Published on
November 2, 2024

We are stoked to welcome Neeraj Poddar (X, LinkedIn) to the NimbleEdge team as our new VP of Engineering! With a remarkable background in building infrastructure products for massive scale, Neeraj has previously co-founded Aspen Mesh and led the engineering team at Solo.io where he also spearheaded Istio, one of the largest and foundational open source projects in the cloud-native ecosystem. In this blog post, we share insights from a conversation with him, outlining the vast experience he brings to the organization and why NimbleEdge's vision resonated strongly with him.

Q: Welcome to the team Neeraj! Can you share a bit about your professional journey prior to NimbleEdge?

Sure, I have had an interesting journey working at various early-stage startups from building novel wireless communication systems to networking infrastructure products geared to solve application security and reliability at massive scale for enterprises. I had the privilege of building and leading engineering teams at Aspen Mesh and Solo.io where we were working on the bleeding edge of cloud innovation. Our products helped enterprises across verticals unlock the true value of cloud-native technologies by enabling them to deploy their applications faster, securing them and obtaining the massive scale & reliability needed to meet their growing customer needs. 

I also had the opportunity to lead and help grow the Istio open-source community which is now one of the most widely adopted service mesh platforms in the world. For me, the best part of all of these experiences is being able to create innovative products in a rapidly changing landscape that deliver value to our customers and the opportunity to grow & mentor team members in their professional journey. 

Q: What drew you to NimbleEdge? Which aspects of our vision resonate with you the most?

I believe great things happen when a confluence of technologies/trends emerge and start to converge around a core idea. With a) the rapid advancement in GenAI capabilities driven by frontier labs, b) hardware availability to meet the AI requirements for compute both in cloud & on devices, and c) the influx of large volumes of data generated by mobile app/cloud explosion, we can all see the trajectory that AI is taking and the impact AI will have in the next five years. What’s missing in this puzzle is how AI will be delivered at scale to end-users without creating bottlenecks at the infrastructure or cloud layer. For me the simplest answer is sometimes the best one and in that vein I believe the majority of the AI use cases in the future will be solved on the hardware running on our mobile devices which will be able to provide enhanced personalized experiences at higher performance while being privacy aware.

When I met Varun his vision for NimbleEdge perfectly aligned with the kind of problems I wanted to solve and be at the forefront of building deep-tech innovation from India. During the interview process, I also met NimbleEdge’s advisors, investors and customers which further reinforced my belief that the platform we are building at NimbleEdge will truly unleash the full potential of AI at scale for billions of users. I’m very grateful and excited to be part of this journey and to help build the future of AI. 

Q: When you’re not leading the charge at NimbleEdge, how do you unwind from work?

I like playing high-intensity games to unwind so I love playing squash or basketball whenever I can. Being a foodie I prefer to gain back those calories spent playing by trying out new restaurants and cuisines.

Conclusion

With Neeraj at the forefront of our engineering efforts, we're more motivated than ever to continue our journey towards building the NimbleEdge platform for delivering AI at scale!

To learn more about what we're building, visit nimbleedge.com or reach out to contact@nimbleedge.com

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SOLUTIONS

Unleash the power of personalized, real-time AI on device

Read your users' mind with personalized, truly real-time GenAI augmented search, copilot and recommendations

Boost conversion and average order value by delivering tailored, GenAI powered user experiences, that adapt in real-time based on user behavior

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Nimble Edge Use Cases Graphical Representation
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Elevate gamer experience with GenAI augmented copilot and real-time personalized recommendations

Improve gamer engagement and cut dropoff with GenAI driven experince, personalized to to incorporate in-session user behavior

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Nimble Edge Use Cases Graphical Representation
Deliver engaging user experiences with real-time GenAI driven co-pilot, search and recommendations

Optimize content discovery using GenAI, with highly personalized user experiences that adapt to in-session user interactions

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Nimble Edge Use Cases Graphical Representation
Use Cases

Leverage the Intelligent Edge for Your Industry

Fintech

Betterment in transaction success rate through hyper-personalized fraud detection
Fintech
Fraud detection models that try to flag fraudulent transactions (applies to all the FinTech apps)
Speed & Reliability issues with transactions in non-real time ML systems on the cloud limit personalization levels, as it operates with Huge Costs of running Real-Time ML systems on the Cloud
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E-Commerce

Increase in models’ performances lead to a rise in Conversion carts with higher order size
E-Commerce
Search & Display recommendation models for product discovery for new and repeat orders Personalized offers and pricing
The non Real-time/Batch ML processing doesn't serve highly fluctuating or impulsive customer interests. Organizations need real-time ML systems but it is impossible to implement and scale them on the cloud with even five times the average cloud cost.
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Gaming

See uplifts in game retention metrics like gaming duration, completion, game cross-sells and LTV
Gaming
Contest SelectionMatchmaking and Ranking Cross-contests recommendationPersonalized offers and pricing
As a result of cloud’s limited infrastructure in providing scalability with respect to ML model deployments and processing in real-time, gaming apps adopt non real-time/batch processing that negatively affects click-through rates, game duration, completion, cross-sells, and lifetime value of players.
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Healthcare

Savings in the privacy budget with privacy preserving encryption algorithms
Healthcare
Personalized Search recommendations (Exercises, Nutrition, Services, Products)
User engagement metrics, customer acquisition and retention, NPS, and other business app metrics suffer. On-device/Edge processing can be a great solution but the data processing capacity is inherently limited due to resource constraints of edge devices.
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Travel & Stay

Increase in average booking value with new and repeat customers with higher NPS & savings in cost of acquisition
Travel & Stay
Search/Service recommendation models  + Personalized offers and pricing
NimbleEdge’s HOME runs real-time ML - Inference & Training - on-device, ensuring performance uplifts in Search/Service recommendation and Personalized offers/pricing models at 1/5th of the cost to run them on the cloud.
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