Job Description
As a Machine Learning Engineer (Fresher), you will participate in the entire development lifecycle of an AI model, from research and experimentation to putting it into actual operation:
- Model Research & Training:
- Participate in building, training, and optimizing Deep Learning models for the Computer Vision field (product defect detection, dimension measurement, classification…) combined with hardware/equipment from the mechanical engineering department.
- Research and design algorithms for Delivery Route Optimization and intelligent logistics coordination for domestic flower transport services.
- Research and improve Machine Learning algorithms for recommendation systems and time-series data processing from IoT sensors.
- Model Deployment (MLOps):
- Package models and build high-performance service APIs using FastAPI and Docker.
- Gradually approach and support the deployment and management of the model lifecycle on AWS and MLflow platforms.
- Data Management: Participate in building collection, pre-processing workflows, and coordinating data annotation to ensure quality for the training process.
- Cross-departmental Collaboration: Discuss and coordinate closely with mechanical engineers, system engineers (Web/Backend), and operations (Logistics/Sales) to successfully integrate AI/ML models into production lines, management software, and the group’s supply chain.
Job Requirements
- Education: Degree in Computer Science, Data Science, Artificial Intelligence (AI), Math-Informatics, or related technical fields (New graduates/students preparing to graduate who can work full-time are accepted).
- Professional Knowledge & Technology:
- Strong foundation in basic Machine Learning/Deep Learning and Image Processing.
- Experience with coursework, personal projects, or internships using at least one of these CV libraries/frameworks: PyTorch, TensorFlow, OpenCV, YOLO, Ultralytics, RF-DETR.
- Understanding or good mindset regarding optimization algorithms (Algorithms, Operations Research) or Graph Theory is a major advantage for the Route Optimization field.
- Good programming mindset with Python. Knowledge of Docker, FastAPI is a major advantage.
- Understanding or desire to learn about MLOps, AWS, and MLflow.
- Soft Skills:
- Proactive in work, capable of self-study, and reading/understanding scientific papers to apply in practice.
- High sense of responsibility, ready to share and support colleagues in a small team.
- Languages:
- English: Ability to read and understand technical documents in English. Good English communication skills are a major plus (international working environment).
- Japanese: Certificate from N3 or higher is a major plus.
Benefits
- Participate directly in all stages from R&D research to actual deployment (Production) of an international-standard AI/ML product.
- Starting Salary: from 12,500,000 VND to 19,500,000 VND (Vietnamese Dong), depending on the candidate’s actual ability.
- Degree Allowance: 1,200,000 VND for candidates with a Master’s degree.
- Foreign Language Allowance (Japanese):
– N1: 1,800,000 VND/month
– N2: 1,200,000 VND/month
– N3: 500,000 VND/month
- Opportunities to learn, exchange, and develop yourself with OTANI personnel at many branches around the world such as Japan, Dubai, Taiwan, Germany, and Singapore.
- Programs to encourage and provide financial support for advanced education.
- Full benefits: Insurance, paid leave as per state regulations.
- Working Hours: 8:00 ~ 17:00 or 9:00 ~ 18:00 (Monday - Friday).
Contact
Please send your CV (including GitHub or Portfolio of AI/ML projects done, if any) to us via:
