At CryptoForge Labs, I rank model responses by quality, relevance, and alignment to create preference datasets for RLHF training. I also design evaluation pipelines for reasoning, coherence, and factual consistency.
I develop Python scripts to automate evaluation workflows, analyze results, and generate performance reports. I annotate and audit reasoning chains to identify errors, biases, and improvements in model-generated solutions.
Previously, at NexusChain Technologies, I analyzed and annotated LLM reasoning traces and evaluated open-ended responses using structured rubrics. At LedgerPeak Solutions, I collected and organized data, assisted with labeling and quality checks, and supported data pipelines.
