2 months ago
Singapore, SingaporeMid Level
Responsibilities
- Apply AI engineering, delivery, and governance expertise to solve complex client and citizen problems.
- Develop and deploy end-to-end AI solutions, including LLM and agentic systems or ML engineering and MLOps workflows.
- Work with tools for prompt engineering, fine-tuning, responsible AI evaluation, model training, deployment, monitoring, and retraining.
- Build and maintain relationships with clients, colleagues, and partners across varied contexts.
- Continuously develop technical knowledge and skills.
Requirements
- At least 3 years of relevant experience in artificial intelligence, machine learning, data science, or software engineering.
- At least one hands-on end-to-end AI development and deployment implementation.
- At least 3 years of implementation experience with serverless computing, CI/CD, containerisation, Infrastructure as Code, code version control, and automated testing.
- Exposure to AWS, Azure, or GCP cloud services.
- Hands-on experience with LLMs and agentic tooling such as LangChain, LangGraph, AutoGen, CrewAI, OpenAI API, Anthropic API, AWS Bedrock, Google Vertex AI, Azure ML, Hugging Face Transformers, MLflow, Dataiku, MS Fairlearn, or Google PAIR.
- Hands-on experience with ML development and deployment tools such as Jupyter, PyTorch, TensorFlow, Scikit-learn, AWS SageMaker, MLflow, Docker, Kubernetes, Terraform, Ansible, and Datadog.
- Knowledge of NLP, computer vision, recommendation systems, reinforcement learning, and time series forecasting is desirable.
- Experience or exposure to experiment tracking, reproducible training runs, model registries, deployment monitoring, and MLOps pipelines using tools such as Weights & Biases, Kubeflow, AWS SageMaker Pipelines, Azure ML Pipelines, or Google Vertex AI Pipelines.
- Exposure to agentic AI concepts such as multi-agent systems, tool use, RAG pipelines, or autonomous task execution is highly desirable.
Tech Stack
AnsibleAWSAzureDatadogDockerGoogle Cloud PlatformHugging Face TransformersKubernetesMLflowPyTorchscikit-learnTensorFlowTerraform
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