AI Engineer

• Harness your expertise in AI engineering, delivery, and governance to solve complex problems and deliver transformative change for clients and citizens.
• 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.

• At least 3 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.
• 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.
• Exposure to one or more cloud services: AWS, Azure or GCP.
• Candidates should demonstrate strength in either AI Engineering or ML Engineering (see below). Strength in both is a plus.

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

• Practical hands-on experience with ML development and deployment tooling: Jupyter, PyTorch, TensorFlow, Scikit-learn, AWS SageMaker, MLFlow, Docker, Kubernetes, 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 MLFlow, Weights & Biases, Kubeflow, AWS SageMaker Pipelines, Azure ML Pipelines, or Google Vertex AI Pipelines.

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/

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.