Designed and deployed LangGraph multi-agent pipelines for enterprise data integration into Neo4j graph schemas, reducing data onboarding time by 60%

Engineered LLM-driven agents to evaluate data quality rules and auto-generate SQL remediation queries, reducing manual data-fix cycles by 8 hours per week

Built Cypher traversal workflows over hierarchical schemas and delivered structured JSON reports to downstream teams, reducing ad-hoc query development time by 70%

Built production RAG chatbot and Text-to-SQL workflows that improved response accuracy by 40% and automated manual reporting, eliminating 2 FTEs of effort

Deployed ML retraining pipelines on SageMaker with CI/CD automation, reducing deployment cycles from 3 weeks to 2 days and achieving 99.5% uptime

Fine-tuned GPT and LLaMA 2 models using LoRA adapters and implemented advanced prompt engineering techniques, improving training time by 40% and retrieval relevance by 25%