AI Engineer I Vietnam (12 mths contract)

Your Role

• Harness your expertise in AI engineering and data engineering to design, develop, and deploy AI/ML solutions on Microsoft Azure for clients.
• Based in Vietnam, working closely with our regional and Singapore-based teams to deliver impactful AI solutions.
• Make an outstanding contribution to a world-class AI and data team.
• Develop and maintain relationships with a broad range of clients, colleagues, and partners across a variety of contexts and formats.
• Invest continuously in building and extending your knowledge and skills.

Your Background

• At least 3–7 years of relevant working experience in fields related to artificial intelligence, machine learning, data science, or software engineering.
• Completed at least one hands-on AI development and deployment end-to-end on production.
• Exposure to agentic AI concepts such as multi-agent systems, tool-use, RAG pipelines, or autonomous task execution is highly desirable.
• At least 3 years of implementation experience with serverless computing, CI/CD, containerisation, Infrastructure as Code, code version control and automated testing.
• Hands-on experience with Microsoft Azure cloud services, including Azure Machine Learning, Azure OpenAI Service, Azure Cognitive Services, Azure Data Factory, Azure Synapse Analytics, and Azure DevOps.
• Candidates should demonstrate strength in either AI Engineering or ML Engineering (see below). Strength in both is a plus.
• Pro-active, curious personality with a strong desire to learn and grow.
• Good English communication skills, ability to communicate with clients and stakeholders effectively.

You should demonstrate strong proficiency in either AI Engineering or ML Engineering / MLOps. Proficiency in both is a significant advantage.

AI Engineering (LLM & Agentic Systems)

• Practical hands-on experience with LLMs and agentic tooling: LangChain, LangGraph, AutoGen, CrewAI, OpenAI API, Anthropic API, Azure OpenAI Service, Azure ML, Hugging Face Transformers, MLFlow, Dataiku, MS Fairlearn.
• Experience with prompt engineering, fine-tuning, and responsible AI evaluation.

ML Engineering / MLOps

• Practical hands-on experience with ML development and deployment tooling: Jupyter, PyTorch, TensorFlow, Scikit-learn, Azure Machine Learning, MLFlow, Docker, Kubernetes (AKS), Terraform, Ansible, Datadog.
• Broad knowledge of NLP, computer vision, recommendation systems, reinforcement learning and time series forecasting.
• Exposure to model experiment tracking and training workflows: hyperparameter tuning, experiment logging (e.g. MLFlow, Weights & Biases), and reproducible training runs.
• Exposure to the MLOps lifecycle: data versioning, model training pipelines, experiment tracking, model registry, deployment, monitoring, and retraining triggers — using tools such as Azure ML Pipelines, MLFlow, Weights & Biases, or Kubeflow.