Mamta Singh

Lead ML Engineer

Bengaluru, Karnataka, India3 yrs 9 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in Machine Learning and Deep Learning technologies.
Proven experience in image processing and video compression.
Strong background in AI research and development.

Stackforce AI infers this person is a Machine Learning Engineer with a focus on AI and image processing.

Contact

mamta26ba@gmail.com LinkedIn

Skills

Core Skills

Machine LearningArtificial Intelligence (ai)Deep LearningImage Processing

Other Skills

AlgorithmsArduinoCC ProgrammingCommunicationComputer VisionConvolutional Neural Networks (CNN)Data AnalysisData AnalyticsData EngineeringData ScienceData StructuresDigital ElectronicsEnglishGenerative AI

Experience

Qualcomm

2 roles

Senior Machine Learning Engineer

Promoted

Nov 2024 – Present · 1 yr 4 mos · Bengaluru, Karnataka, India · On-site

Machine LearningArtificial Intelligence (AI)

Machine Learning Engineer

Jul 2022 – Nov 2024 · 2 yrs 4 mos · Bengaluru, Karnataka, India · On-site

Machine LearningArtificial Intelligence (AI)

Centre for artificial intelligence & robotics, drdo

Research Intern

Jun 2021 – Jul 2021 · 1 mo · Bengaluru, Karnataka, India · Remote

Canon india

Deep Learning Intern

May 2018 – Oct 2018 · 5 mos · Bengaluru, Karnataka, India · On-site

6 month Project based Internship at Canon India Systems Development Center
Worked on Image Denoising with Convolutional Autoencoder Models using Tensorflow
Implemented and trained three convolutional models with different losses- VGG-19 Perceptual loss, MSE loss and DSC (Direct symmetric skip connections) enabled model with MSE loss.
Improved the video compression algorithm (HEVC) by removing the noise from video-frames before video encoding.
Tech stack : Python, Tensorflow, HM Encoder, HEVC

PythonTensorFlowImage ProcessingDeep Learning

Defence research and development organisation (drdo)

Research Intern

May 2017 – Jun 2017 · 1 mo · Delhi, India · On-site

Material Classification in Hyper-spectral Images
Developed a novel slope feature based algorithm by converting the spectrum into binary code.
Results were compared with other spectral matching algorithms such as Spectral angle mapper, Spectral Information Divergence, Normalized Correlation and SVM (Support Vector Machines).