1 year ago
Taipei, TaiwanSenior
Responsibilities
- Architect and operate resilient ML job execution frameworks for training, inference, and post-processing workflows.
- Develop API services and developer tooling to orchestrate ML workflows on Kubernetes.
- Build scalable batch pipelines for large-scale ML training and evaluation.
- Design and maintain data infrastructure with monitoring and observability for high availability.
- Create tooling that accelerates ML experimentation and production workflows.
- Collaborate with ML scientists to turn research prototypes into reliable, scalable, user-facing AI products.
- Lead cloud infrastructure design and operations on GCP using managed compute, storage, messaging, database, and analytics services.
- Define and implement CI/CD pipelines for automated deployments.
- Apply distributed computing and parallel programming to optimize resource utilization and system performance.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related technical field is required; a master’s degree is preferred.
- 5+ years of hands-on experience in ML platform engineering, MLOps, or data infrastructure deploying enterprise-grade machine learning systems at scale.
- Expert proficiency in Python, Java, or Go, with strong data structures and algorithm design foundations.
- In-depth experience with AWS or GCP and cloud-native service management.
- Proven mastery of Docker containers and Kubernetes cluster management, including provisioning, autoscaling, and deployment practices.
- Strong understanding of the ML lifecycle, including training, prediction, evaluation, backtesting, and feedback loops.
- Familiarity with Git workflows and Linux-based development environments.
- Preferred qualifications include MarTech or customer-centric domain experience and architectural leadership of complex cross-team platform initiatives.
- Preferred experience includes deep learning, Kubeflow, MLflow, AWS SageMaker, Apache Spark, Apache Airflow, Argo Workflow, or Luigi.
- Preferred experience includes production ML applications, real-time inference architectures, batch-processing trade-offs, and applications involving LLMs, multi-agent LLM systems, or NLP.
Tech Stack
Apache AirflowApache SparkAWSDatabricksDockerGitGitHub ActionsGoGoogle BigQueryGoogle Cloud PlatformGrafanaHelmJavaJenkinsKubernetesLinuxMLflowPrometheusPythonTerraform
Categories
About Appier
Appier is an AI-native Agentic AI as a Service (AaaS) company that empowers businesses to create value with cutting-edge AdTech and MarTech solutions. Guided by the vision of “Making AI Easy by Making Software Intelligent,” our mission is to help businesses turn Agentic AI into ROI. Founded in 2012, Appier is listed on the Tokyo Stock Exchange’s Prime Market (Ticker: 4180) and operates in 17 cities worldwide, enabling over 2,000 leading companies to enhance marketing performance with the latest AI technology. As AI enablers for our customers in the AI Era, Appier delivers innovative solutions that drive measurable results.