Senior R&D Engineer

AI/ML Model Development and Deployment: Participate in the development, deployment, and optimization of AI/ML models, particularly in the areas of image processing, natural language processing (NLP), and neural networks. Work with and optimize architectures such as Sklearn, Stats, CNN, RNN, GraphNN, YOLO, Transformer, BERT, T5, and their variants.
Data Processing: Execute data preprocessing steps, including image and text processing, normalization, and extraction of key information from diverse datasets. Be flexible in data usage and optimization to ensure the highest efficiency in model performance.
MLOps: Engage in building and deploying large-scale MLOps systems, using frameworks like MLflow and ClearML to manage the model lifecycle from development to deployment.
Containerization: Employ Docker for containerizing applications and environments, facilitating consistent and straightforward model deployment.
Source Control and CI/CD: Utilize Git/GitLab for source control and implement CI/CD systems to automate testing and deployment processes for models.
Research and Development: Contribute to large-scale AI/ML research and development projects, proposing and testing innovative, optimized solutions for complex problems.
Model Deployment: Perform model quantization and conversion to formats such as ONNX Runtime, TFLite, TensorRT, etc.. to optimize performance across different deployment environments.
Service Development: Develop AI/ML services, with proficiency in at least one technology such as RabbitMQ, Apache Kafka, or SparkML for data processing and model deployment in production environments.
Teamwork and Independence: Demonstrate the ability to work independently as well as collaborate effectively with team members. Be adaptable and proactive in ensuring tasks are completed on time.

100% salary in 2 months probation
Free parking card
Participation in social insurance with 100% base salary
Company trip, team building
Internal activities and clubs (year-end party, sports day, men's/women's football club, ...)
Benefits from the Union (gift for Tet holidays, birthdays, weddings, childbirth, ...)
International working environment
Health care 24/7 insurance
Career Development Opportunity

Technical Expertise: Experience in image processing, text processing, preprocessing techniques, and neural networks. Extensive knowledge of architectures like CNN, RNN, GraphNN, YOLO, Transformer, BERT, T5, and their variants.
Programming Skills: Proficiency in Python. Experience with R or C++ is a plus.
Mathematical Foundation: A strong foundation in mathematics, particularly in areas like probability, statistics, linear algebra, and optimization.
Experience: A minimum of 2 years of experience in Machine Learning or AI.
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
Source Control and CI/CD: Experience with Git/GitLab and setting up CI/CD systems.
Experience in natural language processing, information extraction, and object detection.
Containerization and MLOps: Experience with Docker and MLOps frameworks such as MLflow and ClearML to manage and deploy AI/ML models effectively.
Data Flexibility: Flexibility in data usage and optimization to maximize the effectiveness of AI/ML models.
Model Development: Ability to independently develop, innovate, or optimize models ranging from simple to complex.
Model Deployment: Experience with model quantization and conversion to ONNX Runtime, TFLite, TensorRT to optimize deployment.
Service Development: Experience with messaging and data processing systems like RabbitMQ, Apache Kafka, or SparkML.
Academic Contributions: Preference will be given to candidates who have made significant contributions to AI/ML research, including scientific publications.
Project Experience: Preference for candidates who have participated in large-scale projects involving image analysis, Natural language processing, Objects detection, Objects recognitions, OCR and the application of Large Language Models (LLMs), RAG.
Additional Preferences: Candidates with experience in building and deploying MLOps systems, Data Warehouses, Data Lakes, and those who have contributed to AI/ML research are highly preferred.
Supplementary Skills: Creative thinking, quick learning ability, and the capacity to apply new techniques in AI/ML. Strong communication skills and effectiveness in crossdisciplinary collaboration.