Reponsibility
• Actively clean, process, and validate data streams for ML model consumption. Contribute to the implementation of MLOps best practices (CI/CD) and risk mitigation strategies as guided by the engineering team.
• Actively develop and implement Python scripts and small utilities for model features and basic algorithm testing. Ensure development practices align with model fairness and ethical guidelines.
• Establish and track technical Key Performance Indicators (KPIs) to measure model effectiveness post-deployment. Document and report potential technical risks identified during implementation.
• Create and maintain a single source of truth for all active carrier/vendor technical requirements and specifications. Support the definition of project scope, timelines, and milestones.
• Act as the central liaison between engineers, data scientists, and business stakeholders. Translate incoming high-level partner requests into clear, detailed, and actionable engineering specifications and tickets.
• Provide regular, concise updates for stakeholders on project progress, key metrics, and any blockers.
Minimum Qualification
• Proficiency in programming Python, Database(SQL, Vector Database) and GCP(Google Cloud Platform).
• Familiar with AI Agent, Agentic AI, MCP, A2A, Context Engineering
• Experience with implementing machine learning algorithms in practice at scale.
• Good communication skills and a positive attitude to work with stakeholders.
• Proficient English speaking, reading, and writing ability.
• Minimum 7 years experience in an AI-related role.
Working location:
Banqiao or Xin-dian