David Dang
@daviddang
Senior generative AI engineer building production LLM/RAG systems for healthcare, e-commerce, finance, and real-time intelligence.
United StatesWhat I'm looking for
I’m a Senior AI / Generative AI Engineer focused on delivering reliable, real-time AI products. I’ve built production AI systems across healthcare, e-commerce, finance, and travel—turning complex data into actionable intelligence.
At McKesson, I built end-to-end RAG pipelines using LangChain and vector databases to power clinical knowledge retrieval and pharmaceutical supply-chain intelligence. I also engineered AWS ingestion pipelines (S3, Glue, Redshift), reducing feature retrieval latency by 50% and improving medication demand forecasting accuracy.
Previously at Amazon, UnitedHealth Group, and JPMorgan Chase, I developed large-scale AI data pipelines, recommendation systems, and LLM API deployments. I’ve worked on transformer fine-tuning, Spark MLlib modeling, and CI/CD automation to support robust uptime and rapid rollouts.
My professional ethos is engineering rigor with experimentation: I’ve led A/B testing of retrieval and recommendation strategies, implemented automated labeling workflows with clinical review checkpoints, and improved reliability (99.5% pipeline reliability). I’m energized by building systems that stay accurate in production while continuously improving through feedback and testing.
Experience
Work history, roles, and key accomplishments
Built end-to-end RAG pipelines with LangChain and vector databases for near real-time clinical knowledge retrieval and drug availability insights. Engineered AWS ingestion workflows and ML demand forecasting, reducing feature retrieval latency 50% and achieving 99.5% CI/CD pipeline reliability.
Developed scalable retail product search and recommendation agents by fine-tuning transformer models to improve response time and recommendation quality. Built CI/CD for LLM APIs on AWS Lambda and API Gateway to enable robust uptime and rapid version rollouts.
Consolidated patient and claims data to power NLP pipelines for conversational access to medical records. Re-architected ETL for end-to-end lineage and versioning to achieve 99.5% operational reliability and reduce data inconsistencies 35% using dbt testing.
Built Java/Spring microservices exposing ML-driven insights, enabling real-time integration of agent outputs into fraud detection systems. Optimized SQL feature queries to cut inference preparation latency 35% for downstream AI models.
Education
Degrees, certifications, and relevant coursework
Massachusetts Institute of Technology
Bachelor's Degree in Information Technology, Information Technology
2014 - 2018
Earned a Bachelor's Degree in Information Technology at the Massachusetts Institute of Technology from 2014 to 2018.
Tech stack
Software and tools used professionally
Availability
Location
United StatesAuthorized to work in
Job categories
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