全职 Regional AI Engineer 工作, 薪水, ZUS COFFEE 公司招聘中 - Ricebowl

 Qualifications & Education

• Bachelor’s degree in Computer Science, Information Technology, Software Engineering, Data Science, Artificial Intelligence, or a related field.
• 1–3 years of experience in software development, AI engineering, automation engineering, or a related technical role.
• Hands-on experience building applications using LLMs or AI APIs.
• Strong analytical and problem-solving skills.
• Ability to independently research, prototype, test, and deploy new AI technologies.
• Good communication and interpersonal skills.
• Ability to work independently while managing multiple AI projects.
• Strong attention to detail and focus on reliability and quality.
• Experience working with business stakeholders and translating business requirements into technical solutions is preferred.

Required Skillsets - AI & LLM

• Basic to intermediate experience with LLMs and Generative AI.
• Experience with at least one major AI platform/API such as OpenAI, Claude, Gemini, or equivalent.
• Understanding of:
• Prompt engineering
• Structured outputs
• Function/tool calling
• Context windows
• Token usage
• Embeddings
• RAG concepts
• AI Agents

Required Skillsets - Programming

• Basic to intermediate programming experience in Python or JavaScript/TypeScript.
• Ability to read, modify, debug, and maintain existing code.
• Understanding of software development fundamentals such as:
• Functions
• APIs
• Error handling
• Authentication
• Data structures
• Logging

Required Skillsets - API & Data

• Good understanding of REST APIs & JSON.
• Familiar with webhooks and asynchronous API interactions.
• Basic to intermediate knowledge of SQL and databases.
• Understanding of data transformation and validation.

Required Skillsets - AI Application Development

• Experience building AI-powered applications, chatbots, assistants, or automation is preferred.
• Familiarity with RAG / vector databases is a plus.
• Familiarity with MCP (Model Context Protocol) or AI Agent frameworks is a plus.
• Experience integrating AI with n8n, Lark, or other automation platforms is a plus.

Required Skillsets - Engineering & DevOps

• Familiar with Git/GitHub and basic software development workflows.
• Basic understanding of deployment, environment variables, authentication, and application configuration.
• Familiarity with cloud services such as AWS, Azure, or GCP is a plus.
• Experience with Docker/containerized applications is a plus.

Position Responsibilities

AI Solution Development

• Design, develop, and maintain AI-powered solutions to support business operations across regional markets.
• Build AI applications using LLMs, AI APIs, AI agents, RAG, and other AI technologies.
• Integrate AI capabilities into existing business systems, platforms, and workflows.
• Develop solutions such as:
• AI assistants and chatbots
• AI agents for task automation
• Document understanding and information extraction
• AI-powered classification and routing
• Knowledge-based Q&A / RAG applications
• AI summarization and content generation
• AI decision-support tools
• Evaluate different AI models and services based on accuracy, cost, latency, security, and business requirements.

LLM & AI Integration

• Integrate commercial and open-source AI models through APIs and SDKs.
• Work with platforms such as OpenAI, Claude or equivalent AI services.
• Build and maintain prompt templates, structured outputs, tool calling, function calling, and AI workflows.
• Connect AI solutions with internal systems through REST APIs, webhooks, databases, and automation platforms.
• Explore and implement AI Agent / MCP-based solutions where appropriate.

AI Automation

• Combine AI capabilities with automation platforms such as n8n, Lark, and other internal automation tools.
• Identify business processes where AI can reduce manual work, improve response time, or increase operational efficiency.
• Build AI-assisted workflows that can understand information, make structured decisions, and trigger downstream actions.
• Work closely with Automation Engineers to integrate AI components into end-to-end automation solutions.

AI Operations & Maintenance

• Monitor AI applications and identify issues related to:
• Model responses
• Prompt failures
• API errors
• Latency
• Token usage and cost
• Data quality
• Incorrect or unexpected outputs
• Perform basic debugging and root-cause analysis.
• Continuously improve AI solutions based on user feedback and production performance.
• Establish appropriate AI evaluation, testing, monitoring, and quality-control mechanisms.
• Maintain documentation covering architecture, prompts, models, APIs, data sources, and operational procedures.

AI Governance & Security

• Ensure AI solutions follow company requirements for data privacy, access control, security, and responsible AI usage.
• Understand the risks associated with confidential business data being sent to external AI services.
• Implement appropriate controls for sensitive data, permissions, and AI-generated outputs.
• Ensure AI applications have appropriate human review or approval mechanisms where required.

Regional Collaboration

• Work with regional business, operations, product, technology, and data teams to understand AI opportunities and requirements.
• Translate business requirements into practical AI solutions.
• Support deployment and adoption of AI solutions across different countries and markets.
• Understand regional differences in languages, processes, systems, and business requirements.
• Communicate technical limitations, risks, and trade-offs clearly to stakeholders.
• Escalate architecture, data, security, or AI-quality issues early.