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$ cat jobs/lead-ai-engineer-temus-f1b4b2d4eabc.json

Lead AI Engineer

Temus·APAC·Singapore·senior
dockerkubernetesterraformansibleawsazuretensorflowpytorchscikit-learnllmnlpcomputer vision
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Temus is a Temasek-backed consulting firm providing digital transformation solutions for the private and public sectors. W e 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.

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