Atul Kashyap

Senior Software Engineer

Bengaluru, Karnataka, India5 yrs 4 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in full-stack development and deep learning.
Published research on AI model compression at international conferences.
Strong problem-solving skills with a focus on algorithms.

Stackforce AI infers this person is a Fullstack Developer with a strong focus on AI and machine learning technologies.

Contact

Skills

Core Skills

Machine LearningDeep Learning

Other Skills

Algorithm AnalysisAmazon Web Services (AWS)Apache KafkaArtificial Intelligence (AI)Computer NetworkConvolutional Neural Networks (CNN)Data Structure and AlgorithmData StructuresDatabase Management System (DBMS)FlaskGitGitHubJSONJSON Web Token (JWT)Java

About

I have keen interest in software development and likes algorithms and love to solve complex problems through my programming and software design skills. I like working on ideas, refining them and enjoy transforming them to useful products through code. Particularly interested in full-stack development, deep learning and working on breakthrough new technologies.

Experience

5 yrs 4 mos

Total Experience

1 yr 4 mos

Average Tenure

3 yrs 10 mos

Current Experience

Yubi

2 roles

Senior Software Engineer

Promoted

Aug 2022 – Present · 3 yrs 10 mos · Bengaluru, Karnataka, India · On-site

Software Engineer

Aug 2022 – Sep 2023 · 1 yr 1 mo · Bengaluru, Karnataka, India · On-site

Briq

Software Engineer

Sep 2021 – Jun 2022 · 9 mos · Pune, Maharashtra, India

Bosch engineering and business solutions

Intern

Mar 2021 – Jul 2021 · 4 mos · Karnataka, India

Kle technological university - hubballi (india)

Undergraduate Student Researcher

Jan 2020 – Jun 2020 · 5 mos · Karnataka, India

We proposed a methodology that compresses large AI models(DNN or CNN) and improves the inference time such that it can be deployed on edge-devices.
The accuracy loss of the proposed algorithm drops by only 0.44 percent after Pruning and Quantization is applied.
Later, we convert our work into research paper “AI Model Compression for Edge Devices Using Optimization Techniques” and it was accepted at three international conferences :
1. International Conference on Cybernetics, Cognition, and Machine Learning Applications (ICCCMLA) Conference 2020.
2. International Conference on Data Science, Machine Learning & Applications 2020(ICDMLA).
3. Fourth International Conference on Computing and Network Communications (CoCoNet'20).
Publication Link: https://link.springer.com/chapter/10.1007/978-3-030-68291-0_17