Temus

Digital transformation for public and enterprise

AI Engineer 1

Applied Machine LearningConfirmed live in the last 24 hours

ContractVietnamNo salary listed

About the Job

Requirements

At least 3–7 years of relevant working experience in fields related to artificial intelligence, machine learning, data science, or software engineering.
Have completed at least one end-to-end hands-on AI development and deployment project in production.
Have at least 3 years of implementation experience with serverless computing, continuous integration and continuous delivery, containerisation, Infrastructure as Code, code version control, and automated testing.
Have 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.
Demonstrate strength in either AI Engineering or ML Engineering; strength in both is an advantage.
Have good English communication skills and be able to communicate effectively with clients and stakeholders.
Have practical hands-on experience with large language models and agentic tooling, including LangChain, LangGraph, AutoGen, CrewAI, OpenAI API, Anthropic API, Azure OpenAI Service, Azure Machine Learning, Hugging Face Transformers, MLFlow, Dataiku, and Microsoft Fairlearn.
Have experience with prompt engineering, fine-tuning, and responsible AI evaluation.
Have practical hands-on experience with machine learning development and deployment tooling, including Jupyter, PyTorch, TensorFlow, Scikit-learn, Azure Machine Learning, MLFlow, Docker, Kubernetes (AKS), Terraform, Ansible, and Datadog.
Have broad knowledge of natural language processing, computer vision, recommendation systems, reinforcement learning, and time series forecasting.
Have exposure to model experiment tracking and training workflows, including hyperparameter tuning, experiment logging (e.g. MLFlow or Weights & Biases), and reproducible training runs.
Have exposure to the MLOps lifecycle, including 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.

Responsibilities

Design, develop, and deploy AI and machine learning solutions on Microsoft Azure for clients, using AI engineering and data engineering expertise.
Work closely with regional and Singapore-based teams to deliver AI solutions.
Develop and maintain relationships with clients, colleagues, and partners across a variety of contexts and formats.
Continuously build and extend knowledge and skills.

Desired Qualifications

Exposure to agentic AI concepts such as multi-agent systems, tool use, retrieval-augmented generation pipelines, or autonomous task execution is highly desirable.
Proficiency in both AI Engineering and ML Engineering/MLOps is a significant advantage.
Proactive and curious approach with a strong desire to learn and grow.

Temus is a Temasek-backed consulting firm providing digital transformation solutions for the private and public sectors. We aspire to be a strategic partner in realising the Singapore Government’s Smart Nation vision. We are headquartered in Singapore and have more than 400 employees across a wide range of disciplines in strategy, design, architecture, technology, data & AI.

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.

Temus is an equal opportunities employer. We welcome applications from all. We do not discriminate by race, religion, belief, ethnicity, origin, disability, age, partnership status, sexual orientation, or gender identity.

We see the diversity of our team as a strategic advantage, and we work actively to maintain it.

By applying for this role, you have read and acknowledge the data privacy statement via this link - temus.com/job-applicant-data-protection/

About the Company

Temus

Digital transformation for public and enterprise

Temus delivers digital transformation services for public-sector organisations and private enterprises by combining strategy, service and product design, agile delivery, low-code development, cloud engineering, data and artificial intelligence. It works across mission-critical domains such as healthcare and defence, building tailored digital platforms and applications. The company emphasizes involving users, customers, citizens and employees in the design of practical technology solutions and uses an end-to-end approach from strategy to delivery. Temus differentiates itself by its strong focus on human-centered, cross-domain delivery within public sector and enterprise contexts, and by operating as part of a broader digital transformation ecosystem anchored in Singapore. The goal is to help organisations modernise operations, improve services, and enable impactful public and enterprise outcomes through practical technology solutions.

Company size: 201-500
Company stage: Not listed
Total funding: Not listed
Headquarters: Singapore, Singapore
Founded: 2021

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