Intern - Operation Improvement

Project Title

Operation Flow Systemization with AI Agents

Project Description

This internship focuses on supporting the design and development of standardized operation flows through AI-enabled systemization. The intern will develop Python-based automation and AI agents that analyze operational data, identify inefficiencies, and support decision-making in a manufacturing environment.

Scope of Work

• Analyze existing operational and manufacturing workflows to identify systemization opportunities
• Design and develop AI agents for:
  • Operational flow assistance
  • Alerting and exception handling
  • Bottleneck and inefficiency identification
• Develop backend logic, automation scripts, and AI workflows using Python
• Work with MSSQL databases for:
  • Data extraction
  • Transformation and aggregation
  • Integration into AI and analytics pipelines
• Apply relevant Python libraries, including (exposure expected, not mastery):
  • pandas, numpy – data processing and analysis
  • matplotlib, seaborn, plotly – data visualization
  • scikit-learn – basic machine learning and pattern detection
  • LLM / AI frameworks (e.g., LangChain, LlamaIndex, or equivalent internal AI tools)
• Collaborate with engineering, planning, and manufacturing teams to translate operational problems into AI-driven solutions

Supporting Teams

• Manufacturing & Production Teams
• Industrial Engineering (IE) / Operation Engineering (OE) / Planning

Key Deliverables

• Python-based automation or AI-agent prototypes addressing real operational use cases
• SQL queries and data pipelines supporting analytics and AI logic
• AI-agent workflows for monitoring, recommendations, or decision support
• Dashboards, reports, or visual outputs supporting operational insights
• Technical documentation covering system flow, logic, and assumptions

Stretch Goals

• End-to-end AI-assisted operation flow (data → insight → recommendation → action)
• Advanced analytics such as:
  • Bottleneck detection
  • Throughput or cycle-time analysis
• Simple web interface or API for interacting with AI agents (e.g., Streamlit or Flask)

Requirements

• Currently pursuing a degree in:
  • Information Technology
  • Computer Engineering
  • Computer Science
  • Data Science or related fields
• Fundamental knowledge or strong interest in:
  • Python programming
  • SQL and relational databases
  • AI, machine learning, or agent-based systems
• Interest in manufacturing, operations, or smart factory transformation
• Strong problem-solving mindset and willingness to learn in a real production environment