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IBM India Pvt. Ltd.
Frontend Developer
June 16, 2026 – Present
ai-demos
December 1, 2025 – Present
The repository covers python based AI example apps like iris and fraud detection which use traditional machine learning models with ONNX runtime.
View Projectpower-access-cloud
November 24, 2025 – Present
This repository provides a full-stack application with a self-service portal to access Power systems temporarily.
View Projectproject-ai-services
November 10, 2025 – Present
Project AI Services will help deploy e2e AI use cases that solve business problems for Power Users.
View Projectibmcloud-nuke
June 7, 2022 – August 21, 2023
Delete the resource in the IBM Cloud account
View Projectcontainer-image-updater-action
October 31, 2021 – February 28, 2024
container-image-updater-action — GitHub repository
View Projectraksh-agent
December 23, 2019 – March 15, 2020
Raksh Agent - to be used with Raksh project
View Projectraksh
December 23, 2019 – August 26, 2021
Seamlessly use VM based TEEs with Kubernetes for data-in use protection
View Projectopenbmc-automation
September 21, 2015 – June 6, 2016
Test OpenBMC Distribution, moved to - https://github.com/openbmc/openbmc-test-automation
View ProjectCultural Fit Analysis
The candidate's projects are primarily backend/infrastructure focused (Go, Python, Shell, Dockerfile) with a strong emphasis on AI/ML, cloud infrastructure, and system-level tools. While there is one project ('power-access-cloud') that mentions JavaScript and SCSS, the overall portfolio does not strongly align with a dedicated 'Frontend Developer' target role. The diversity of projects indicates an interest in various technical domains, but the breadth of skills leans heavily towards backend and DevOps, which may not be a direct cultural fit for a pure frontend team.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's experience level is listed as 0, which contradicts the current role as 'Frontend Developer' at IBM India Pvt. Ltd. starting in 2026. This discrepancy makes it difficult to accurately assess operational fit.