Operations Expert with Python for AI Supply Chain Task Design

If you are a supply chain professional with a technical edge, this opportunity offers a unique way to apply your operational expertise to the rapidly evolving field of artificial intelligence. Toptal is currently scouting for experienced practitioners to develop high-level benchmarking tasks for AI agents. Rather than standard administrative work, this role requires you to translate your real-world logistical knowledge into digital simulations that test whether an AI can effectively manage complex supply chain planning.

About the role

The core objective of this position is to bridge the gap between traditional operations management and advanced machine learning. You will be responsible for creating "Terminal-Bench" tasks, which are rigorous scenarios designed to push AI agents to their limits.

Your task is to take a genuine challenge you have faced in your career—such as a complex production planning issue—and build it into an end-to-end test. This includes preparing the necessary data, defining the environmental constraints, and ensuring the task is realistic enough to mimic actual ERP (Enterprise Resource Planning), MES (Manufacturing Execution Systems), or WMS (Warehouse Management Systems) operations.

You will act as an architect of assessment tools, ensuring that the AI is being evaluated against the same standard of logic and accuracy required of a human planner. Key duties include:

• **Task Design:** Translating authentic industry scenarios into digital challenges that can be completed within a 60 to 75-minute timeframe.
• **Technical Implementation:** Using Python or other scripting languages to create automated verification scripts that objectively measure whether an AI’s output is correct.
• **Documentation:** Drafting clear, unambiguous instructions, standard operating procedures (SOPs), and constraints that outline the "rules of the game" for the AI.
• **Edge Case Integration:** Incorporating realistic data inconsistencies and common human planning errors to see how the AI handles the messy reality of supply chain management.
• **Iterative Refinement:** Collaborating with reviewers to improve task clarity, ensure data consistency, and guarantee that the verification process is fully reproducible in a command-line environment.

What they're looking for

This is not a theoretical role; Toptal is looking for individuals with deep, hands-on experience in the trenches of supply chain management. You should be able to demonstrate:

• **Operational Depth:** A minimum of three years of active experience in production planning or supply chain management where you were directly involved in the process, not just supervising it.
• **Technical Literacy:** Proficiency in Python or a similar language, with a solid grasp of how to manipulate CSV and JSON files, interact with APIs, and work within command-line environments.
• **Domain Expertise:** Familiarity with the specific logic behind ERP and WMS exports. You should be able to identify exactly where a planner might make a mistake and how to programmatically detect that error.
• **Clarity of Thought:** The ability to translate complex, intuitive planning decisions into rigid, rule-based documentation.

In the South African market, roles that combine domain-specific operational expertise with programming skills—often referred to as Supply Chain Data Analytics or Optimization roles—are increasingly lucrative. Compensation for specialist roles involving AI benchmarking is typically project-based or contract-heavy, and applicants can expect higher rates than standard entry-level operations jobs due to the specialized nature of the technical requirement. This path is excellent for those looking to pivot into Data Science or AI Operations (AIOps) while maintaining their footing in the manufacturing and logistics sector.

• 3+ years of hands-on supply chain or production planning work (not only studying, selling or managing it), or a PhD with active research in the subfield.
• Did the work within the last 3 years and can name the systems, tools, data and rules used.
• Personally executed a process where each case is decided by written rules such as SOPs, planning rules or constraints.
• Worked inside systems of record (ERP, WMS) and with their exports or reports.
• Can describe edge cases, data inconsistencies and common planner errors, and how a mistake would be caught.
• Technical fluency in Python or another language and the command line, plus files, CSV/JSON, APIs or databases.
• Available to start around November 4, 2026.