• Develop, train, evaluate, and deploy machine learning and AI models to solve real-world business problems.
• Implement end-to-end AI pipelines, including data preprocessing, feature engineering, model training, validation, and inference.
• Collaborate with Software Engineers to integrate AI models into production systems via APIs or microservices.
• Analyze model performance, identify failure cases, and iteratively improve accuracy, robustness, and efficiency.
• Debug and troubleshoot issues in data pipelines, model behavior, and production inference workflows.
• Take ownership of assigned AI features or models, delivering reliable solutions within agreed timelines.

Technical Skills

• 3+ years of professional experience in AI, Machine Learning with hands-on involvement in production AI systems.
• Strong practical experience in building and deploying machine learning models, including:
• ○ Strong hands-on experience in building, training, and deploying deep learning models for Natural Language Processing (NLP) tasks, including text classification, information extraction, and name entity extraction.
• ○ Practical experience with modern LLM frameworks and toolchains, such as DSPY, LangChain, or similar ecosystems, is highly preferred.
• Solid programming skills in Python, with experience using common ML libraries (e.g., NumPy, scikit-learn, Pytorch).
• Familiarity with RESTful APIs for integrating AI models into backend systems; hands-on experience with ONNX-based deployment or Rust-based inference services is highly advantageous.
• Ability to read technical papers and documentation in English; strong communication skills in Vietnamese; Japanese proficiency is an advantage.

Professional Attributes

• Strong analytical and problem-solving skills with a data-driven mindset.
• Ability to balance model performance, system constraints, and delivery timelines.
• Comfortable working under guidance from senior AI engineers while independently handling well-defined tasks.
• Collaborative attitude, working effectively with cross-functional teams including Product, Engineering, and Data.
• Strong sense of ownership for model quality, reliability, and continuous improvement.