Function-Call Trace Annotation for Conversational Agents
Labeled 500+ tool-use chains (arguments, returns, error states) that drive fine-tuning and regression tests for API-enabled chat agents.
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For the past two years I’ve specialised in human-in-the-loop training of large-language models, supplying >6 000 high-quality feedback samples that power code assistants and chatbots. My core work spans RLHF — ranking and critiquing model-generated code, diagnosing compiler/runtime errors, and crafting gold-standard fixes — using pipelines on Scale AI, Toloka and Labelbox. These datasets feed reward models that raise functional accuracy, reduce bias, and harden models against unsafe outputs. In parallel, I lead multilingual prompt QA (English ↔ Arabic) and machine-translation post-editing. My safety focus includes extensive red-teaming and jailbreak testing—simulating adversarial prompts that expose bias or policy leaks before deployment.
Labeled 500+ tool-use chains (arguments, returns, error states) that drive fine-tuning and regression tests for API-enabled chat agents.
Post-edited 100 k+ words (EN↔AR) in MTPE workflows and ran content-safety sweeps, cutting critical error rate below 2 %
Audited Arabic/English prompts for tone and cultural fit; produced the style guide now used across OneForma’s ISAAC translation projects
Ranked and critiqued 900+ code generations, authored reference solutions and unit tests, and annotated JSON function-call schemas
Bachelor's Degree, Computers And Informatics
Principal AI Architect (Consultant)
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