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About

Pronoy Chopra is a seasoned technologist and innovator with over 15 years of experience in software engineering, solutions architecture, and AI/ML systems development. His professional journey spans a diverse range of sectors including neuroscience, telecom, agriculture technology, and cloud computing. Currently serving as a Senior Solutions Architect in AI/ML at Amazon Web Services, Pronoy has played a pivotal role in securing $400M in business opportunities by leveraging advanced Generative AI platforms. He has built robust, serverless AI tools using Amazon Bedrock and Claude3, contributed significantly to AWS global outreach through conferences and blogs, and holds an AWS Machine Learning Specialty certification. Pronoy’s background as a Principal Software Engineer at Kernel showcases his deep technical expertise in neuroscience and embedded systems. At Kernel, he engineered real-time data acquisition systems, optimized high-performance computing pipelines, and led the development of distributed control and visualization platforms. His ability to bridge hardware and software domains is evident in his contributions to camera integration, neuroscience simulation frameworks, and centralized data infrastructures. Earlier in his career, Pronoy held impactful roles in the telecom sector, contributing to VoIP platforms and custom PBX solutions. His academic roots in electronics and communication engineering laid the foundation for his hands-on experience with embedded systems, AVR/RISC architectures, and control systems. Notably, he is the inventor of a patented wireless mesh network system tailored for agricultural applications and has contributed to the Google Summer of Code program. Pronoy’s resume highlights his multidisciplinary expertise, innovative mindset, and leadership in scaling engineering teams and projects. He has developed numerous backend and frontend systems using technologies like Django, FastAPI, Flask, React, and Rust. His cloud and deployment capabilities span AWS services, Docker, Celery, and other DevOps tools. He also possesses a strong academic background with a Master’s in Electrical & Computer Engineering from the University of Oklahoma, where he worked on image retrieval systems and control automation. Through public speaking engagements, hackathons, open-source contributions, and mentoring, Pronoy continues to impact the broader tech ecosystem. His unique ability to fuse low-level programming with modern cloud-native architecture makes him a rare hybrid engineer capable of solving complex challenges across domains. Pronoy Chopra is a highly accomplished software engineer and solutions architect with over 15 years of diverse experience across software development, AI/ML systems, neuroscience technology, IoT, and cloud computing. Known for bridging low-level embedded systems with cutting-edge cloud-native platforms, Pronoy brings a rare combination of hardware proficiency and software architecture expertise that enables him to tackle some of the most complex challenges in the technology space. Currently serving as a Senior Solutions Architect for AI/ML at Amazon Web Services (AWS), Pronoy has directly influenced over $400 million in business opportunities through his contributions to large-scale AI/ML initiatives. He is credited with building and maintaining internal ChatGPT-like tools using serverless architecture powered by Amazon Bedrock and Claude3 models. His work extends beyond technical implementation—he is a frequent speaker at AWS events like re:Invent and re:Inforce and has authored widely-read technical blogs on Generative AI, IoT, and cybersecurity. Pronoy also holds an AWS Machine Learning Specialty certification and is a core member of the AI/ML and IoT technical communities within AWS. Before joining AWS, Pronoy was the Principal Software Engineer at Kernel, a pioneering neuroscience company focused on developing next-generation brain-computer interfaces. There, he was instrumental in integrating high-speed cameras for neural data capture, optimizing simulation pipelines using AWS and Docker, and creating centralized data infrastructures that cut operational costs by thousands of dollars monthly. His technical leadership led to the creation of a live dashboard in React and Flask for GPU and EC2 monitoring, a neuroscience task-runner using Django and Celery, and the migration of company-wide analysis workflows to JupyterLab, thereby significantly increasing efficiency. Earlier in his career, Pronoy worked in the telecommunications sector at Apeiron Systems, where he contributed to building a Twilio-like Telecom-as-a-Service platform. His responsibilities included working with vendor APIs (e.g., Verizon, AT&T) and debugging complex VoIP and packet drop issues. Pronoy also served as a freelance consultant and product inventor in the agri-tech space, developing mesh network-based smart farming systems and delivering sustainable software solutions for governmental projects in India. His academic background is equally impressive, with an MS in Electrical & Computer Engineering from the University of Oklahoma, where he worked on content-based image retrieval systems and automation tools using Python and LabView. He also holds a Bachelor’s in Electronics & Communication Engineering and has published and patented technologies in areas ranging from wireless networks to AI/ML optimization. Throughout his journey, Pronoy has remained committed to innovation and education—hosting public hacking sessions, mentoring teams, and scaling engineering departments. His technical skill set spans Python, JavaScript, C++, Rust, Flask, Django, AWS, Docker, Celery, PostgreSQL, Redis, and more. He is also experienced in embedded systems programming, scientific computing, and full-stack development. In essence, Pronoy Chopra exemplifies the modern technologist—deeply technical, highly innovative, and capable of navigating and integrating across the full technology stack, from microcontrollers to machine learning APIs.

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Skills

Experience

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Senior Startups Solutions Architect - Generative AI

Amazon Web Services (AWS)

Mar-2021 to Present
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Software Developer

University of Oklahoma (OU)

Jul-2015 to Nov-2015
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Software Engineer III

Apeiron Systems, Inc.

Nov-2015 to May-2018
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Graduate Research Assistant

University of Oklahoma (OU)

Apr-2013 to May-2015
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Principal Software Engineer

Kernel

May-2018 to Mar-2021
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Hardware/Software Engineer

Cultivate Labs

Jul-2015 to Jan-2016
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DEVELOPER

IIT Delhi (Indian Institute of Technology, Delhi)

Apr-2012 to Jan-2013
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Contractor

Google

Mar-2011 to Aug-2011

Education

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University of Oklahoma (OU)

M.Sc. in Electronics and Computer Engineering

Passout Year: 2015
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Jaypee University Of Information Technology (JUIT)

Electrical, Electronics and Communications Engineering in Electrical, Electronics and Communications Engineering

Passout Year: 2011

Publication

  • dott image March, 2021

Kernel Flux: a whole-head 432-magnetometer optically-pumped magnetoencephalography (OP-MEG) system for brain activity imaging during natural human exp...

MEG based on optically-pumped magnetometry (OP-MEG) operates with miniaturized, wearable insulation, in contrast to massive cryogenic dewars for SQUID-MEG, and allows placement of the sensor...

Peer-Reviewed Articles

ASD-Pipeline: An Ensemble Machine Learning Framework Integrating Feature Selection, Behavioural Clustering, and Class Rebalancing for Accurate Autism ...

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a variety of behavioral and cognitive patterns. Early and precise detection is critical in enabling timely interventions. Conventional classification models frequently exhibit poor generalization due to irrelevant features, unstructured behavioral data, and severe class imbalance. Despite current advances in machine learning for ASD detection, current models do not integrate adaptive feature selection, behavioral grouping, or imbalanced class handling in a unified, end-to-end pipeline. The lack of incorporation frequently results in suboptimal performance and limited interpretability. This study proposes a new ensemble-based framework called ASD-Pipeline, which integrates flexible feature selection, hybrid clustering, synthetic minority oversampling, and ensemble voting classification to improve the predictive performance for ASD identification. The proposed ASD-Pipeline framework uses a five-stage process to improve the accuracy of autism spectrum disorder prediction. First, the dataset is normalized utilizing Min-Max scaling to guarantee that the feature ranges remain consistent. Next, feature selection is performed utilizing FlexiFeat, an ensemble method integrating filter-based (CfsSubsetEval with BestFirst), wrapper-based (WrapperSubsetEval with GreedyStepwise), and embedded (ReliefF with Ranker) techniques to maintain only the most pertinent feature. The ClusterGroup stage uses K-Means clustering (k=5) and DBSCAN improvement (ε=0.5, minPts=3) within each cluster to create behavioral groups and remove outliers. The ReBalance stage uses Cluster-SMOTE to tackle class imbalance by producing synthetic samples for the minority class and a balanced dataset. Finally, the ASDClassifier stage involves training an ensemble of Logistic Regression, Support Vector Machine, and Gradient Boosting classifiers that are combined using soft voting. Metrics used to assess the model include accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). The proposed ASD-Pipeline surpassed existing models, achieving a significantly higher accuracy of 96.18% compared to previous techniques ranging from 76.80% to 90.60%. It also scored 91.51% precision, 91.63% recall, 95.57% F1-score, and 92.51% specificity. These findings emphasize the pipeline's efficacy in enhancing generalization and tackling difficulties such as feature relevance, behavioral grouping, and class imbalance for ASD prediction. The ASD-Pipeline offers a reliable, interpretable, and modular machine learning solution for ASD prediction. Its incorporated method tackles critical challenges in feature relevance, behavioral variability, and data imbalance, rendering it a promising tool for healthcare practitioners and researchers seeking data-driven insights into early ASD detection.

Projects

Oct-2012 to Present

Never Alone

This game was designed and partially developed for Ludum Dare 22 gamedev sprint competition. As the design was considered potent, the game is still in development.
...see more

Certificates

Issued : Sep 2023
  • dott image By : Amazon Web Serv...
  • dott image Event : Amazon Web Serv...
AWS Certified Machine Learning - Specialty
Credential ID be8b6ea6-ea46-4ec9-b7ff-c82d8be02ff7 https://www.credly.com/badges/be8b6ea6-ea46-4ec9-b7ff-c82d8be02ff7 Earners of this certification have an in-depth understanding of AWS machine learning (ML) services. They demonstrated ability to build, train, tune, and deploy ML models using the AWS Cloud. Badge owners can derive insight from AWS ML services using either pretrained models or custom models built from open-source frameworks.
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Patent

  • dott image Chemical Science
Wireless Sensor Mesh Network With Dual Homed Router & Control Through Mobile Devices
Assignee:

USA

Filing Country:

United States

Filing Month:

Apr 2019

Application No:

US 14/683,069

Patent No:

US 14/683,069

Publication status:

Published

Publication Date:

Apr 2025

Inventor(s): Self

Scholar9 Profile ID

S9-042025-2411369

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Authors

PRONOY CHOPRA
PRONOY CHOPRA

Senior Startups Solu...