ML/AI Engineer
Resume:
NGUYEN LAN HUONG
# **************.*******@*****.*** +84-946****** § huonglarne
EXPERIENCE
ML engineer
* Moirai z 2022
• Improved and deployed an audio match-
ing software for movie-audio process-
ing companies.
AI engineer
* VTON z 2022
• Developed and deployed a virtual
clothes try-on web application.
AI engineer
* Aimesoft z 2021
• Improved the recognition algorithm
for an employee face recognition soft-
ware.
• Participated in R&D projects:
face animation according to voice
recording, image captioning in Viet-
namese.
AI research assistant
* USTH ICT Lab z 2020
• Participated in lung nodule segmenta-
tion research project.
LANGUAGES
• English: Fluent - 8.0 IELTS
• Vietnamese: Mother tongue
EDUCATION
B.S. in Information and
Communication technology
* University of Science and
Technology of Hanoi z 2018 - 2021
• Training language: English
• GPA: 3.7/4.0
• Degree classification: Very good
SKILLS
Machine learning/Deep learning
• Computer vision:
• Face recognition.
• Object detection and segmentation.
• Content transfer (motion, style etc).
• Audio processing:
• Voice recognition.
• Audio fingerprinting.
• Keyword spotting.
• Natural language processing:
• Vision-language models.
• Deep learning:
• Network implementation.
(Pytorch, Tensorflow, MXNet)
• Model training and parameter tuning.
(Optuna, Pytorch Lightning)
• Knowledge distillation / student-teacher training. MLOps
• Software engineering:
• CI/CD pipelines.
• Test-driven development.
• Deployment:
• Docker.
• FastAPI.
• Torchserve. Triton.
• SQL.
• AWS. GCP.
PROJECTS
Virtual clothes try-on web application
• Overview: Led a small team that laid the first bricks for a brand-new AI product.
Contributed to the roadmap and vision of the product.
• Technologies: Self-attention GAN. FastAPI. Docker.
• Contributions:
• Researched existing AI solutions to the problem.
= Chose and deployed a suitable core model for the product.
• Designed an architecture of separate services for each component model.
Implemented APIs for serving deep learning models.
= Allowed independent development and maintenance.
• Designed a suite of unit and integration tests for each service.
= Helped constructing the CI/CD pipeline.
• Created a standardized and shareable development environment.
= Increased development team’s productivity.
Audio matching software
• Overview: Deployed a 3-year stalling project into production. Made extensive improvement to the performance and maintainability of the product.
• Technologies: Audio fingerprinting. Github actions. AWS Lambda.
• Contributions:
• Optimized parameters of machine learning algorithm.
= Improved the model’s test accuracy by 36%.
• Designed a suite of unit and integration tests.
= Ensured continuous integration for CI/CD pipeline.
• Refactored the legacy code base.
= Better system maintainability.
= Reduced cost for developing new features.
Face recognition software
• Overview: Researched and implemented solutions to improve the perfor- mance of an existing AI product.
• Technologies: ResNet. ArcFace.
• Contributions:
• Fine-tuned the pre-trained face recognition model to optimize for Asian faces.
= Better sensitivity when identifying Vietnamese employees and customers.
• Implemented adaptive threshold for face embedding search.
= Improved the model’s accuracy by 4.8%.
• Used face clusters search.
= Reduced recognition speed for large face database. Recommendation engine for book website
• Technologies: SQL. Scikit-learn.
• Contributions:
• Designed a database to store the website’s data about products and customers.
= Optimize for data processing and query.
• Classified users into interest groups and designed a ranking score.
= Developed the book recommendation algorithm.
Lung nodule segmentation research project
• Technologies: R-CNN. U-Net.
• Contributions:
• Preprocessed the lung CT scan dataset.
= Clean dataset with easy-to-process format for future research.
• Proposed and implemented a 3D approach for nodule segmentation.
= Laid ground to future research paths in the project.
Contact this candidate