Sudhamsh K.

Software Engineer

Bengaluru, Karnataka, India3 yrs 7 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in Machine Learning and Natural Language Processing.
Experience in developing healthcare-related AI solutions.
Proficient in Generative Neural Networks for image generation.

Stackforce AI infers this person is a Data Scientist with a focus on Healthcare and Machine Learning.

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Skills

Core Skills

Machine LearningNatural Language Processing (nlp)Generative Neural Networks

Other Skills

AlgorithmsArtificial Intelligence (AI)BootstrapC (Programming Language)C++Data AnalysisData ScienceData StructuresDeep LearningGoogle colabHTML5IntelliJ IDEAJavaJavaFXLinux

About

My keen interests are Mathematics, Machine Learning, Data Science, Computer Vision, Natural language processing, Programming.

Experience

Millennium

Software Engineer

Jun 2025 – Present · 9 mos · Bengaluru, Karnataka, India

Apt research private limited

Software Engineer

Jul 2022 – May 2025 · 2 yrs 10 mos · Bengaluru, Karnataka, India

High frequency trading (HFT) firm

Concertai

Data Scientist

May 2021 – Jul 2021 · 2 mos

Worked on benchmarking HealthCare APIs that can be used for NER, Assertion and Negation detection, relationship extraction.
Implemented ML and DL models for temporal relationship extraction in clinical texts using contextual embeddings from BERT on i2b2 temporal relationship extraction dataset.
Created an ensemble model using Rule based approach, Natural Language Inference model and BERT based classifier to improve overall model performance.

Natural Language Processing (NLP)Machine LearningDeep LearningData Science

Emproto technologies

Machine Learning Intern

Apr 2020 – Jun 2020 · 2 mos · Bengaluru, Karnataka, India

Worked on generating realistic images of fashion datasets (such as clothes, shoes, jewelry) by training Generative adversarial networks (GANs) on a virtual machine having Nvidia Tesla V100 GPU which acts as a virtual designer.
Implemented model that is used for generating realistic images and which can further be used for mixing the styles of images that are generated to create a new realistic image as output based on the weights we input.

Generative Neural NetworksMachine LearningDeep Learning