AI Engineer (LLM Systems, Prompt Engineering, RAG)
TextPrompt Response Writing SFT
Designed and deployed agentic LLM workflows combining semantic retrieval, ranking, and prompt orchestration for large language models. Automated multi-stage retrieval-augmented generation (RAG) pipelines using GPT-4 and LangChain for high-signal evidence extraction at scale. Established structured prompt design, validation checkpoints, and quality metrics to ensure high model reliability.
• Implemented systematic experimentation with chunking strategies and embedding configurations.
• Improved retrieval quality and reduced noise in large document corpora by 78%.
• Tuned and validated semantic matching workflows for production ML and LLM systems.
• Own full lifecycle from research prototyping to deployment and iterative optimization.
Designed and deployed agentic LLM workflows combining semantic retrieval, ranking, and prompt orchestration for large language models. Automated multi-stage retrieval-augmented generation (RAG) pipelines using GPT-4 and LangChain for high-signal evidence extraction at scale. Established structured prompt design, validation checkpoints, and quality metrics to ensure high model reliability. • Implemented systematic experimentation with chunking strategies and embedding configurations. • Improved retrieval quality and reduced noise in large document corpora by 78%. • Tuned and validated semantic matching workflows for production ML and LLM systems. • Own full lifecycle from research prototyping to deployment and iterative optimization.
2025 - Present