Jeniya Tabassum
Senior Applied Engineer at Deepgram
Dallas-Fort Worth Metroplex United States
Summary
🤩
Rockstar🎓
Top SchoolJeniya Tabassum is a Senior Applied Engineer with a PhD in Computer Science and a decade of experience building production-grade ML and agentic AI solutions for enterprises. She has led GenAI adoption at IBM and delivered high-impact NLP and speech models at Amazon and Deepgram, including fine-tuned LLMs and speech-to-text/text-to-speech systems optimized for noisy and spoken-language domains. Skilled in PyTorch, TensorFlow, Hugging Face and MLOps, she designed self-hosted GPU deployments and contributed to AWS SageMaker examples and SDKs to accelerate Hugging Face and Inferentia workflows. Her research background produced state-of-the-art results in domain-specific NER, relation extraction and temporal tagging, and she brings that rigor to scalable production pipelines with strong accuracy and recall. Based in the Dallas–Fort Worth area, she combines deep research pedigree with hands-on deployment experience and a knack for turning complex enterprise requirements into reusable AI assets.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at The Ohio State UniversityBachelor of Science (BSc) Computer Science, Bachelor of Science (BSc) Computer Science at Bangladesh University of Engineering and Technology
Stackoverflow
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Github Skills (18)
pytorch10
python10
machine-learning10
sagemaker10
amazon-sagemaker10
huggingface10
deep-learning10
aws10
jupyter-notebook10
data-science9
cicd9
trainings8
tensorflow7
mlops7
reinforcement-learning5
Programming languages (4)
Github contributions (5)
aws/amazon-sagemaker-examples
Nov 2021 - Mar 2022
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:86 reviews, 5 commits, 41 PRs in 3 months
Contributions summary:Jeniya primarily focused on updating and modifying existing Jupyter notebooks related to training machine learning models on Amazon SageMaker. Their contributions involved adjusting training parameters like epochs in language model notebooks to enhance performance. The user also updated data sources to ensure accessibility across all regions and removed unnecessary dependencies. These changes directly impacted the training process, improving the efficiency and usability of the example notebooks.
pythonjupyter-notebooktrainingawssagemaker
aws/sagemaker-python-sdk
Sep 2021 - Mar 2022
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:MLOps Engineer
MLOps EngineerContributions:102 reviews, 13 commits, 51 PRs in 5 months
Contributions summary:Jeniya primarily focused on improving the integration and support for Hugging Face models within the SageMaker ecosystem. This included adding support for specific PyTorch versions for the Hugging Face framework, modifying existing code, updating test scripts, and contributing to the documentation generation process. The user also enhanced the testing infrastructure for notebook integration, and the addition of Neuron inference. The user appears to have worked on improving the usability of prebuilt models.
pytorchsagemakerdeployingmxnetpython
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