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Software Developer | Researcher | Innovator | NSIT Student
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Assessing your cultural and operational fit
awesome-deepseek-resources
January 28, 2025 – January 28, 2025
This repository is a collection of awesome projects and resources related to DeepSeek Papers and Research.
View Projectcloud-casino-front-end
December 9, 2024 – December 13, 2024
cloud-casino-front-end — GitHub repository
View Projectcasino_composite_microservice
November 9, 2024 – December 15, 2024
casino_composite_microservice — GitHub repository
View Projectuser_microservice
October 6, 2024 – December 15, 2024
user_microservice — GitHub repository
View Projectroulette_microservice
October 6, 2024 – December 15, 2024
roulette_microservice — GitHub repository
View ProjectCloudRaider
May 30, 2019 – December 5, 2023
A resiliency tool that automates Failure mode effect analysis tests, simplifying complex testing with a behavior-driven development and testing approach. Provides a programmatic way to execute controlled failures in AWS and a BDD way to write test cases, allowing test plans themselves to become test cases that can be executed as is.
View ProjectMACHINE-LEARNING-CN
January 21, 2018 – October 6, 2018
COGNIZANCE - Machine Learning Course @ Coding Ninjas - January'18
View ProjectCultural Fit Analysis
The candidate's personal projects demonstrate a breadth of technical interests, from microservices to machine learning resources. However, the majority of projects are general software development, with only two directly related to machine learning/data science ('awesome-deepseek-resources' and 'MACHINE-LEARNING-CN'). This indicates some alignment with the 'Data Scientist' target role, but the overall project portfolio is not heavily specialized in data science. The lack of professional experience or detailed project descriptions makes it difficult to fully assess cultural fit beyond technical interests.
Soft Skills & Operational Fit
The provided data does not contain sufficient information to assess soft skills or operational fit. Project descriptions are brief, and there are no interview notes or peer feedback.