Lead AI Engineer

About the role

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 Objectives

- Solve the hardest problems with AI to deliver social and economic value.

- Develop and maintain relationships with a broad range of clients, colleagues, and partners across a variety of contexts and formats.

- Build, lead and mentor a world-class team of AI and data engineers.

- Maintain a culture of excellence and lead with confidence, charisma, context, and humility working effectively at all levels.

- Create and deliver technical blogs & thought leadership on AI.

- Invest continuously in building and extending your knowledge and skills.

Your Background

You bring strong capabilities 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, AWS Bedrock, Google Vertex AI, Azure ML, Hugging Face Transformers, MLFlow, Dataiku, MS Fairlearn, Google PAIR.

- Experience with prompt engineering, fine-tuning, evaluation frameworks, and responsible AI tooling.

ML Engineering / MLOps

- Practical hands-on experience with ML development and deployment tooling: Jupyter, PyTorch, TensorFlow, Scikit-learn, AWS SageMaker, MLFlow, Docker, Kubernetes, Terraform, Ansible, Datadog.

- Broad experience of NLP, computer vision, classification & recommendation systems, reinforcement learning and time series.

- Experience designing and managing model experiment tracking and training workflows: hyperparameter tuning, cross-validation, experiment logging (e.g. MLFlow, Weights & Biases), and reproducible training runs.

- Solid understanding and hands-on experience with the MLOps lifecycle: data versioning, model training pipelines, experiment tracking, model registry, deployment, monitoring, drift detection, and retraining triggers — using platforms such as MLFlow, Weights & Biases, Kubeflow, AWS SageMaker Pipelines, Azure ML Pipelines, or Google Vertex AI Pipelines.

Platform & Delivery

- At least 5 years of implementation experience with serverless computing, CI/CD, containerisation, Infrastructure as Code, code version control and automated testing.

- Broad experience of model deployment, canary releases and implementation on cloud.

- Experience of automated data pipelines, data labelling, versioning and exploration.

- Strong ability to develop and maintain relationships amongst clients, colleagues, and partners.