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About

Hrishikesh Mane is a dynamic and accomplished software engineer currently working at Amazon on the Rufus AI team. With a strong foundation in cloud computing, microservices architecture, and full-stack development, Hrishikesh has carved a remarkable career path, moving through some of the most respected names in the tech industry, including AWS, VMware, and iauro Systems. He holds a Master’s degree in Computer Science from Binghamton University and a Bachelor of Engineering in Computer Engineering from Savitribai Phule Pune University. These academic credentials laid the groundwork for his technical prowess, which he has since honed through hands-on experience in real-world software engineering challenges. Hrishikesh began his professional journey at VMware, where he worked in End User Computing (EUC). There, he gained valuable experience in Linux systems, networking, and enterprise virtualization, contributing to VMware’s industry-leading infrastructure technologies. His tenure at VMware also earned him certifications such as the VMware Certified Professional in Desktop and Mobility, and Data Center Virtualization in 2020, further validating his technical expertise. Following this, he transitioned to iauro Systems Pvt. Ltd., taking on the role of Senior Software Engineer. Here, he broadened his skills in JavaScript frameworks, Webpack, REST APIs, and cloud-native application development, helping build scalable and performant systems for various clients. Hrishikesh's capabilities were further sharpened during his time at Amazon Web Services (AWS), first as a Software Engineer Intern in 2023 and then as a full-time Software Engineer. Based in Santa Clara, California, he contributed to critical backend infrastructure, employing technologies such as Java, AWS services, and microservices architectures. His ability to solve complex problems and drive results quickly propelled him within the organization. In January 2025, he advanced to his current position at Amazon as a full-time Software Engineer on the Rufus AI team. His current role likely involves working on cutting-edge artificial intelligence technologies, integrating large-scale machine learning solutions, and deploying them across Amazon’s extensive infrastructure. Hrishikesh is well-versed in technologies including AWS, React, Java, JavaScript, and Microservices, reflecting a balanced skillset in both backend and frontend development. His personal website, ihrishi.vercel.app, and his digital credential profile on Credly showcase his professional achievements and technical capabilities. His activity on professional platforms like LinkedIn shows a collegial and encouraging presence—regularly congratulating peers and expressing gratitude for collaborative experiences. With over 3,500 followers and 500+ connections, Hrishikesh maintains a strong professional network and continues to grow within the tech community. Hrishikesh Mane exemplifies a modern software engineer—technically sound, continuously evolving, and committed to contributing meaningfully in every role he undertakes.

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Skills

Experience

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Software Engineer

Amazon, Sunnyvale

Jan-2025 to Present
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Machine Learning Engineer

ASCP GPUONCLOUD PVT LTD.

Jan-2019 to Jun-2020
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Software Engineering

Amazon Web Services (AWS)

May-2024 to Jan-2025
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Senior Software Engineer

iauro Systems Pvt Ltd. (iauro)

Dec-2021 to Jan-2023
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Mentor for Tensorflow at Google Code-in

Google Code-in (GCI)

Dec-2019 to Jan-2020
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Engineer- End User Computing

Broadcom Inc. (VMWare Inc.), Palo Alto

May-2020 to Dec-2021
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Software Engineer Intern

Amazon Web Services (AWS)

May-2023 to Aug-2023

Education

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Binghamton University (BU)

M.SC in Computer Science

Passout Year: 2022
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Savitribai Phule Pune University

BE in Computer Engineering

Passout Year: 2020

Publication

  • dott image May, 2020

Checxray: A Saas Application for Chest X-Ray Diagnosis

Journal : IJERT - International Journal of Engineering Research & Technology

Training a Deep learning model is a highly computational demanding task and requires a high-end graphics card for parallelism in training a Deep Convolutional Neural Network for numerical ca...

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.

Conference/Seminar/STTP/FDP/Symposium/Workshop

Conference
  • dott image Apr 2020

Computational Intelligence Based Model Detection of Disease using Chest Radiographs

Hosted By:

2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) ,

Vellore, Tamil Nadu, India
There are many diseases associated with lungs or thoracic cavity and the diagnosis of these diseases at once becomes difficult for any medical profusion. The most common way of screening done when the thoracic cavity comes into the picture is Chest Radiography. However, diagnosing multiple diseases from a single scan becomes difficult. This paper proposes an intelligent machine learning-based model which tries to detect 14 chest diseases out from a single radiograph with greater accuracy. This paper makes use of advanced deep learning techniques like neural networks, masking algorithms, etc. to assure higher performances. 10.1109/ic-ETITE47903.2020.484 Hrishikesh Mane Student (B.E.), Department of Computer Engineering, Modern Education Society’s College of Engineering, Pune, India Parag Ghorpade Student (B.E.), Department of Computer Engineering, Modern Education Society’s College of Engineering, Pune, India Vedant Bahel Student (B.E.), Department of Information Technology, G H Raisoni College of Engineering, Nagpur, India
...see more

Certificates

Issued : Dec 2020
  • dott image By : VMware
  • dott image Event : Desktop and Mob...
VMware Certified Professional - Desktop and Mobility 2020
https://www.credly.com/badges/a04fc04d-4ffc-40c6-9522-f4be31ce932d?source=linked_in_profile Administrator DaaS Desktop Management EUC Horizon 7 Mobility Managmenet NEE VMware Horizon Client VMware Workspace Portal vRealize Operations For Horizon vSphere Workforce Mobility
...see more
Issued : Aug 2019
  • dott image By : Qwiklabs
Kubernetes in the Google Cloud
https://www.cloudskillsboost.google/public_profiles/bbb3cfcd-5df4-4662-a493-da652fffd5ca

Scholar9 Profile ID

S9-112024-1206391

Publication
Publication

(1)

Review Request
Article Reviewed

(21)

Citations
Citations

(200)

Network
Network

(1)

Conferences
Conferences/Seminar

(1)

Academic Identity

Academia Profile
Academia Profile

Authors

Hrishikesh Rajesh Mane
Hrishikesh Rajesh Mane

Software Engineering