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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.
Skills & Expertise
JavaScript
Java
Microservices
Amazon Web Services (AWS)
React
Cloud Computing
Java Programming
Microservices Design
Microservices Design
Java Programming
JavaScript Development
React Framework
RESTful APIs
Webpack Tooling
Linux Administration
Networking Basics
Data Center Virtualization
Enterprise Virtualization
Full Stack Development
AWS Cloud Services
AI Integration
Machine Learning
Software Debugging
Agile Development
System Scalability
Infrastructure Automation
Frontend Technologies
Research Interests
Software Engineering
HTTP/HTTPS
Cloud Computing
Microservices Architecture
Full Stack Development
Backend Infrastructure
Frontend Development
Machine Learning
Artificial Intelligence
Java Development
JavaScript Frameworks
React Development
Linux Systems
Networking Technologies
Virtualization Tools
REST API Design
AWS Services
Web Application Engineering
Software Architecture
Scalable Systems
Technical Mentorship
Connect With Me
Experience
Software Engineer
Software Engineering
- ● Led the successful decoupling of a service from a tightly coupled monolith system in Java, enabling the transition toward a robust microservice architecture. Resulted in increased system availability and improved scalability ● Spearheaded the designing and development of a highly efficient service, replacing the existing search platform with Elastic search technology. Implemented a seamless migration process, enhancing search functionality, and improving availability to 99% across 50+ regions in the AWS network ● Added Metrix, Logs, and Alarms to mitigate and troubleshoot Dynamo DB and web services using AWS Cloudwatch, resulting in 50% fewer customer tickets and identifying anomalies preemptively in pre-production regions ● Enforced type safety across microservices REST API via Smithy and code generation, reducing bugs by 15% ● Introduced robust authorization and throttling based on service principals and service level agreements, elevating security in a multi-tenant and multi-region application for more than 1,500 customers Skills: Microservices · Java · Continuous Integration and Continuous Delivery (CI/CD) · Amazon Web Services (AWS) · Elasticsearch · Back-End Web Development
Software Engineer Intern
Senior Software Engineer
- Webpack · JavaScript · React.js · Git · CSS · Docker · TypeScript · Automation · Web Applications · Front-End Development
Engineer- End User Computing
- Skills: Linux · Networking · VMware Horizon View · SQL · Python (Programming Language) · Access Gateway · VMware vCenter · JavaScript · TypeScript
Machine Learning Engineer
- Skills: TensorFlow · Keras · Machine Learning · Python (Programming Language) · Natural Language Processing (NLP)
Mentor for Tensorflow at Google Code-in
- Skills: Machine Learning · Python (Programming Language) · Data Science
Education
Binghamton University (BU)
Savitribai Phule Pune University
Conferences & Seminars (1)
Computational Intelligence Based Model Detection of Disease using Chest Radiographs
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
Certificates & Licenses (2)
Kubernetes in the Google Cloud
https://www.cloudskillsboost.google/public_profiles/bbb3cfcd-5df4-4662-a493-da652fffd5ca
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
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