Kishaloy Halder
Senior Applied Scientist at Amazon Web Services (AWS)
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolKishaloy Halder is a Senior Applied Scientist at AWS with 10 years of experience building and scaling NLP and generative-AI systems, currently focused on safeguarding and grounding LLM-based applications on the Bedrock team. He holds a Ph.D. in Computer Science from NUS and has published in top-tier NLP and IR venues while developing practical solutions across AWS Comprehend, Zalando, and industry roles. His work spans deep learning, topic models, collaborative filtering, and production-grade APIs for large-scale e-commerce and cloud services. An active contributor to the Flair NLP ecosystem, he has improved core classification and embedding components, reflecting a blend of research rigor and hands-on engineering. Based in Seattle, he combines academic depth with product-minded delivery to turn advanced language models into safe, scalable customer-facing features.
9 years of coding experience
7 years of employment as a software developer
Indian Institute of Technology BombayBachelor of Technology (B.Tech.), Computer Science, Bachelor of Technology (B.Tech.), Computer Science at Institute of Engineering & ManagementDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at National University of Singapore
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
transformer-models10
pytorch10
machine-learning10
nlp10
python10
text-classification10
natural-language-processing10
modeling9
model-optimization9
trainings9
word-embeddings9
Programming languages (2)
Github contributions (5)
flairNLP/flair
Oct 2020 - Mar 2021
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Role in this project:
ML Engineer
ML EngineerContributions:7 reviews, 9 commits, 6 PRs in 5 months
Contributions summary:Kishaloy made several contributions focused on the TARSClassifier model within the Flair NLP framework. Their work involved fixing bugs, improving model loading and tokenization, and adding mean/max pooling strategies to the document embeddings. The user also addressed review comments and fixed data splitting logic for the IMDB dataset. Their commits directly modified core model components and embedding strategies, indicating a focus on improving the performance and functionality of the NLP models.
nlppytorchnamed-entity-recognitionartsemantic-role-labeling
kishaloyhalder/homepage
Nov 2016 - Dec 2024
Contributions:15 pushes, 1 branch in 8 years 2 months
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