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.

1. 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.

1. 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.

1. 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.