Requirements
Experience shipping and operating an LLM or agent system used by real customers.
Robust software engineering skills in TypeScript or Python, with ability to work across both.
Experience building evaluations, datasets, experiments, or AI quality systems.
Solid product judgment and ability to turn vague AI quality issues into measurable problems.
Ability to work across data, evaluation methods, model selection, and fine-tuning.
Robust ownership as a senior individual contributor.
Bonus: Experience with multimodal AI, video, media, or creative software.
Bonus: Experience with human labeling, model graders, or fine-tuning.
Bonus: Strong understanding of experiment design and statistics.

Responsibilities
Define quality standards for Cardboard’s agent.
Build trusted evaluation datasets from real product usage.




Build offline and online evaluations using automated checks, model graders, and human review.
Analyze real agent runs and identify recurring failure patterns.
Improve agent quality through better data, evaluation methods, model selection, and fine-tuning.
Build regression checks and release gates for important agent changes.
Track AI quality alongside latency and cost.
Partner with product and engineering teams to ship measurable improvements.

Job Details

Bengaluru, India

Interview Process
Recruiter Screen
Technical Interview
ML & Evaluation Deep Dive
Product & Engineering Interview
Final Interview

📌 Artificial Intelligence Engineer Bengaluru
🏢 ClanX
📍 Bengaluru