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.
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.
- 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 structure, logging, etc.
- 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.
- 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.
- 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.
a Necessity, not a Luxury