AI Engineer

Kyanon Digital is a Vietnam-based tech powerhouse. We deliver world-class solutions to clients across the globe. At Kyanon Digital, we offer end-to-end solutions that encompass every facet of the digital landscape. With the slogan: “Digital Impact that Matters”, this has guided our team of over 300 employees for over 12 years, creating many positive changes for large clients in various industries.

We are seeking a highly skilled AI Engineer to join our team and drive data-driven innovation. The ideal candidate will possess a strong foundation in statistics, machine learning, and programming, coupled with a passion for extracting meaningful insights from complex data. You will play a critical role in developing and implementing data-driven solutions that address complex business challenges.

How You Can Contribute

1. AI Solution Design & Development

• Design, develop, and deploy AI/ML models that address real-world challenges using state-of-the-art (SOTA) techniques.

• Implement and optimize deep learning models across domains such as NLP, Computer Vision, and Generative AI (e.g., LLMs, diffusion models).

• Translate high-level concepts and research outcomes into scalable, production-ready systems.

• Develop reusable modules and model pipelines that align with engineering best practices.

• Collaborate with AI Researchers, AI Engineers and product teams to bring innovative ideas to life.

2. Data Engineering & Modeling

• Handle structured and unstructured data from various sources including images, videos, and text.

• Conduct data cleaning, feature engineering, and augmentation to prepare datasets for model training.

• Apply and fine-tune deep learning architectures (e.g., CNNs, Transformers, GANs, ViTs) for targeted use cases.

• Implement SOTA techniques from academic and industry literature to improve model performance.

3. System Integration, MLOps & Deployment

• Build end-to-end machine learning pipelines for model training, evaluation, deployment, and monitoring.

• Use MLOps tools (e.g., MLflow, Kubeflow, Airflow, DVC) for experiment tracking, model versioning, and reproducibility.

• Containerize ML applications using Docker and deploy via CI/CD pipelines in cloud or on-prem environments.

• Leverage cloud AI/ML platforms (e.g., AWS SageMaker, GCP Vertex AI, Azure ML) for scalable and secure deployment.

• Monitor model performance in production and implement mechanisms for retraining and updates.

4. Collaboration & Continuous Improvement

• Collaborate with cross-functional teams (software, product, business) to align AI solutions with user and business needs.

• Stay updated with the latest trends and advancements in AI, deep learning, and generative models.

• Contribute to internal knowledge sharing and technical documentation.

• Participate in code reviews, propose technical improvements, and mentor junior engineers in AI best practices.

What You Need To Maximize Your Contribution

Education & Experience

• BSc or MSc in Computer Science, AI, Data Science, Software Engineering, or a related technical discipline.

• 4+ years of industry experience delivering AI/ML systems from prototype to production.

• Strong hands-on experience with deep learning and modern ML workflows.

• Exposure to real-world applications in domains such as Computer Vision, NLP, or Generative AI.

Technical Skills

• Proficiency in Python; familiarity with Java or C++ is a plus.

• Strong experience with ML/DL frameworks such as PyTorch, TensorFlow, HuggingFace Transformers, Keras.

• Skilled in building and fine-tuning models for vision (CNNs, ViTs), text (LLMs), or generative tasks (GANs, diffusion models).

• Experience with data processing libraries (Pandas, NumPy), SQL/NoSQL databases, and data pipelines.

• Moderate experience in MLOps practices, including:

• MLflow, Airflow, or DVC for lifecycle management

• Docker and Kubernetes for containerization and orchestration

• Git and CI/CD for continuous delivery

• Familiarity with monitoring tools and logging for deployed ML services.

Soft Skills

• Strong analytical thinking and problem-solving capabilities.

• Excellent communication skills to work with technical and non-technical stakeholders.

• Ability to work independently and in a collaborative environment.

• Eagerness to explore new technologies and a passion for innovation.

• Attention to detail and a mindset for reproducibility and scalability.

Preferred (Nice to Have)

• Experience working with LLMs or foundation models in applied projects.

• Exposure to edge deployment or real-time inference systems.

• Contributions to open-source ML/AI projects or experience presenting at meetups/conferences.

• Domain experience in Smart Retail, Finance, or Digital Assistants.