Lead AI Engineer

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. Ability to develop and deliver client proposals, and build consensus supported by detailed analysis, deep expertise and effective communication. Demonstrated ability to ...