資深後端軟體工程師, Senior Platform Engineer , Software Shared Services

About the Role : We are building the ViewSonic Software Ecosystem, integrating products across all ViewSonic teams to improve user experience with high reusability and cross-team scalability. As an Integration Engineer, you will serve as the bridge between workflows, data pipelines, and cross-team product APIs — transforming PoCs into reliable, reusable, and monitored production services. You will design, implement, and optimize cloud infrastructure to ensure seamless integration with data services, AI services, and product endpoints across teams. Responsibilities : *Design & Develop AI Components -Build reusable AI workflow components using LangFlow / LangChain / LangGraph. -Integrate components with RAG pipelines, knowledge graphs, and external APIs. *System Design -Architect scalable and maintainable systems that can support millions of users. -Ensure clear documentation and well-defined interfaces for cross-team integration. *Data Integration & Serving -Connect with AWS data lakehouse services (S3, Glue, Iceberg, Athena, Redshift). -Develop RESTful APIs and microservices that deliver AI and data services to internal and external consumers. *Collaboration & Enablement -Partner with AI/ML engineers, data engineers, and product teams to craft cross-team solutions. -Lead workshops and create documentation to drive adoption of AI platform features. Requirements : *Must-Have Skills: -Platform Engineering: Strong experience with containerization (Docker, Kubernetes, EKS, ECS) and CI/CD (GitHub Actions, Terraform). -Backend Integration: Expertise in API design, REST/gRPC, and gateway patterns. -Programming Languages: Proficient in at least one of Go, Node.js, or Python. -Observability: Familiarity with LLMOps monitoring tools (Langfuse, Langsmith) and cloud monitoring stacks. -System Design: Experience contributing to the design or operation of software systems supporting millions of users. *Preferred Skills: -Generative AI Engineering: Hands-on experience with LangFlow, LangChain, LangGraph, or similar frameworks. -Data Engineering: Knowledge of data lake/lakehouse concepts and AWS services (S3, Glue, Athena, Redshift, Iceberg). -Certifications: AWS Certified Solutions Architect or Data Analytics Specialty. -Search/Vector DBs: Experience with semantic search and vector databases (Qdrant, Chroma). -Developer Platforms: Contributions to internal platforms, SDKs, or reusable AI tooling. Why Join Us -Contribute to a core platform initiative that powers multiple AI products across the company. -Shape the future of AI solution delivery speed, reusability, and scalability. -Collaborate with a high-impact, cross-functional engineering team. -Build reusable AI infrastructure leveraged across diverse product lines. -Create world-class software applications used globally.