外商資本額179億2700萬員工人數700人
Responsibilities:
Current Context:
Design, develop, and maintain the backend infrastructure for our AI platform, focusing on a multi-agent system architecture.
Develop and optimize prompts for large language models (LLMs) and other AI models.
Implement and manage MLOps/LLMOps pipelines for model training, deployment, and monitoring.
Implement and manage relational (RDB) and NoSQL databases as needed for AI model training and data storage.
Integrate AI models and services with other platform components and applications.
Troubleshoot and resolve AI backend issues, ensuring performance, scalability, and reliability.
Collaborate with Data Engineers, Data Scientists, and other stakeholders to define and deliver AI-powered solutions.
Partner with cross-functional teams globally, communicating platform updates effectively.
Role Briefly: Multi-Agent Systems, Prompt Engineering, LLMOps/MLOps, AI Model Integration, RDB, NoSQL, Backend Development
Expectations for Three Months: Become familiar with our existing technology stacks, not only within your specific role but across the broader data platform ecosystem.
Expectations Within One Year: Contribute to the development of key components of our AI platform, demonstrating expertise in multi-agent systems and prompt engineering. Specific contributions can be discussed.
Who We're Looking For
Non-Technical Skills & Mindset:
Impact-Driven & Results Focused:
Value-Oriented: Focused on delivering solutions that generate significant business value (millions USD impact).
Impact Conscious: Prioritizes work with the greatest technical and business impact. A focus on enabling data consumption through API creation is a plus.
Growth & Learning Mindset:
Cross-Functional Learner: Eager to learn and understand cross-functional knowledge beyond core expertise.
Technology Agnostic Learner: Willing to learn new technologies and adapt to evolving landscapes.
Efficient Learner: Able to leverage AI tools to maximize productivity and accelerate learning.
Best Practice Pragmatist: Loves to follow best practices but understands trade-offs and works around limitations when necessary. Demonstrated pro-activeness through contributions to open-source projects is highly valued.
Collaborative & Global Communicator:
Team Player: Collaborates effectively in global team environments. Adaptable and comfortable working within an Agile environment.
Excellent Communicator (English & Chinese): Fluent in both English and Chinese (Mandarin) to effectively communicate with global teams and stakeholders.
Technical Concepts: We're looking for candidates with a strong grasp of:
Fundamental computer science knowledge
Root cause finding methodologies
Systematic/architectural thinking
Clean code/clean architecture principles and an aversion to over-design
Technical Skills:
Python: Proficient in Python, with experience in AI/ML backend development.
SQL: Solid SQL skills for data management and querying.
Cloud Development: Hands-on experience with GCP, including hybrid environments with on-premises DCs. Experience with AWS or Azure is also acceptable.
Multi-agent System, prompt engineering and basic knowledge for machine learning / deep learning.
Tech Stacks:
Compute & Hosting: GKE & GCE (RedHat), GCP Cloud Run & Cloud Functions
Data Orchestration: GCP Cloud Composer (Airflow)
Data Lakehouse: BigQuery
Data Streaming: Kafka Ecosystem (Confluent Cloud, Debezium, Qlik)
Monitoring & Observability: GCP Monitoring/Logging/Metrics, OpenTelemetry
CI/CD: GitHub Actions, Jenkins
Infrastructure as Code: Terraform
Security: VPC SC & Policy Tags, Customer-Managed Encryption Keys (CMEK), Vault
Containers: Docker, Kubernetes
Data Governance: Collibra
Data Visualization: Power BI