vaibhavi m

Machine Learning Engineer

Bengaluru, Karnataka, India10 mos experience
Most Likely To Switch

Key Highlights

  • Achieved 95% accuracy in face extraction model.
  • Developed innovative sign language translation system.
  • Skilled in both front-end and back-end technologies.
Stackforce AI infers this person is a Machine Learning Engineer with expertise in AI and web development.

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Skills

Core Skills

React.jsDjangoMachine LearningGesture Recognition

Other Skills

HTMLCascading Style Sheets (CSS)Model TrainingHigh Level Of AccuracyYOLOv5CommunicationReal-time RenderingVisualizationAnalyticsSQLCritical ThinkingConflict ResolutionTech Career SkillsSoft SkillsC++

Experience

10 mos
Total Experience
5 mos
Average Tenure
7 mos
Current Experience

Fast code ai

2 roles

Machine learning engineer-1

Nov 2025Present · 7 mos

Graduate Trainee

Sep 2025Nov 2025 · 2 mos

Vaps technosoft pvt ltd

Software developer intern

Jan 2025Jun 2025 · 5 mos · Bengaluru, Karnataka, India · On-site

  • Developed and maintained web applications for the VAPS Management System (VMS), enhancing functionality and user experience.
  • Built responsive, dynamic front-end interfaces using React.js, HTML5, and CSS3, ensuring cross-browser compatibility.
  • Designed and implemented scalable back-end features with Django, integrating APIs and managing database interactions.
HTMLCascading Style Sheets (CSS)React.jsDjango

Accure private ltd bangalore

Data Science Intern

Jun 2023Aug 2023 · 2 mos

  • Developed and optimized a face extraction model using YOLOv5 to accurately identify and extract faces from various forms of identification, such as ID cards and PAN cards.
  • Preprocessed images to enhance model training.
  • Achieved a high accuracy rate of 95%
Model TrainingHigh Level Of AccuracyMachine Learning

Ieee cs bangalore chapter

Research Intern

May 2023Aug 2023 · 3 mos

  • Developed an innovative solution to assist visually impaired individuals by translating sign language gestures into text in real time. To make communication more accurate and effective, the system also included facial expression recognition.
  • Achieved Real-Time results and successfully reduced the error rate of gesture recognition.
  • Published a research paper in IEEE, contributing to the academic and professional community.
CommunicationReal-time RenderingGesture Recognition

Education

PES University

Bachelor of Technology - BTech — Electronics and Communications Engineering

Oct 2021Oct 2025

BASE PU College

Pre-University — Computer Science

Jan 2019Jan 2021

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