AI Engineer & Data Scientist – LLM Output Evaluation and Human-in-the-loop Feedback
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I evaluated and ranked the outputs from large language models (LLMs) such as GPT-4o and Llama for reasoning, factual accuracy, and safety criteria. I designed and implemented large-scale LLM evaluation pipelines, integrating structured workflows and human-in-the-loop feedback for iterative model improvement. This process contributed to enhanced model reliability, reduced hallucination rates, and improved benchmarking efficiency.
• Implemented evaluation frameworks optimizing response quality.
• Collaborated on RLHF-style feedback systems to refine model alignment.
• Leveraged prompt engineering to target error reduction in outputs.
• Used benchmarking metrics to increase efficiency across multiple domains.
I evaluated and ranked the outputs from large language models (LLMs) such as GPT-4o and Llama for reasoning, factual accuracy, and safety criteria. I designed and implemented large-scale LLM evaluation pipelines, integrating structured workflows and human-in-the-loop feedback for iterative model improvement. This process contributed to enhanced model reliability, reduced hallucination rates, and improved benchmarking efficiency. • Implemented evaluation frameworks optimizing response quality. • Collaborated on RLHF-style feedback systems to refine model alignment. • Leveraged prompt engineering to target error reduction in outputs. • Used benchmarking metrics to increase efficiency across multiple domains.
2022 - 2025