AI/ML Engineer
Corning Incorporated2 hours ago
Shanghai, ChinaSenior
Responsibilities
- Design, build, and deploy production-grade AI agents, supervisor agents, and multimodal applications using LangGraph or comparable frameworks.
- Implement agent-tool and agent-data integrations using MCP and enable cross-agent communication using A2A.
- Develop reusable agent patterns, Agent Skills, shared components, and internal packages for standard development workflows.
- Design, build, and deploy machine learning, deep learning, and statistical models for business and AI product requirements.
- Perform data processing, feature engineering, and model and agent evaluation using Python, Databricks, AWS, and GitLab CI/CD.
- Identify data-driven innovation opportunities with business and technical stakeholders.
- Translate loosely defined problems into actionable analytics and AI solutions and contribute to scalable, reusable AI product architecture.
- Develop RAG, multi-agent, tool-using, and multimodal agent systems and support data engineering, pipeline automation, and scalable cloud processing.
- Participate in cross-functional agile development initiatives and help improve workflows, technical practices, documentation, and technology adoption.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
- 5+ years of experience in data analytics, machine learning, or AI solution development.
- Hands-on experience building LLM-powered applications or agentic AI solutions in production or production-like environments.
- Hands-on experience with Databricks, Git-based development workflows, CI/CD pipelines, and cloud-based data platforms such as AWS.
- Experience with LangGraph or comparable agent orchestration frameworks, with familiarity with LangChain, embedding models, vector databases, and RAG architectures.
- Proficiency in Python and SQL; experience with AI-assisted coding tools is a plus.
- Experience with agile development, version control systems, testing, production deployment practices, agent protocols, authorization patterns, and MLOps.
- Experience with data engineering, data pipeline automation, and scalable data processing on cloud platforms.
- Strong systems thinking, communication, collaboration, documentation, adaptability, and stakeholder-management skills.
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