Senior AI Engineer (LLM & Model Training)

Position Overview

The AI Engineer will be responsible for designing, developing, training, and deploying advanced Artificial Intelligence and Machine Learning models to solve complex business and manufacturing challenges. This role requires deep hands-on experience in model training, fine-tuning Large Language Models (LLMs), and building scalable AI systems capable of operating in production environments.

Working closely with Data Engineers, Software Engineers, Manufacturing, Quality, Supply Chain, and Digital Transformation teams, the AI Engineer will transform large-scale structured and unstructured data into impactful AI solutions that drive operational excellence, automation, and innovation across the organization.

This position plays a critical role in enabling VinFast's vision of becoming a data-driven and AI-powered global EV manufacturer.

Key Responsibilities

AI Model Development & Training

• Design, develop, train, and optimize Machine Learning and Deep Learning models for production use cases.

• Fine-tune foundation models and Large Language Models (LLMs) for enterprise applications.

• Build AI solutions leveraging Computer Vision, Natural Language Processing (NLP), Generative AI, Predictive Analytics, and Recommendation Systems.

• Develop and execute model training pipelines, including data preprocessing, feature engineering, model validation, and performance evaluation.

• Conduct experiments to improve model accuracy, robustness, scalability, and inference efficiency.

• Apply transfer learning, reinforcement learning, and advanced deep learning techniques where appropriate.

Generative AI & LLM Applications

• Develop and deploy Generative AI solutions using state-of-the-art foundation models.

• Build Retrieval-Augmented Generation (RAG) systems and AI Agents to support enterprise use cases.

• Optimize prompts, fine-tuning strategies, and inference workflows to improve model performance.

• Evaluate and benchmark various LLMs against business requirements and performance targets.

Production Deployment & MLOps

• Deploy and maintain AI models in production environments.

• Implement model monitoring, performance tracking, retraining, and lifecycle management processes.

• Collaborate with Data Engineering teams to establish scalable data pipelines and feature stores.

• Utilize MLOps best practices for model versioning, deployment automation, and continuous improvement.

• Ensure AI solutions meet reliability, security, and governance requirements.