Vetted OCR Professionals

Chase Bradley

Screened ReferencesModerate rec.

Staff AI Engineer specializing in LLM systems, retrieval, and ML infrastructure

Remote10y exp

“ML/LLM engineer from Cohere who has owned retrieval, reranking, agentic workflows, and internal evaluation infrastructure end-to-end in production. Particularly strong in turning brittle RAG and research-heavy ideas into scalable enterprise systems with grounded outputs, lower customer escalations, and adoption by major clients like Notion and Fujitsu.”

“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”

“Internship at BlueFoxLabs building and deploying an AI/ML RAG system for a biopharma client on top of LibreChat, including an AWS Textract ingestion pipeline and PGVector retrieval deployed to AWS EKS. Demonstrated production-minded scalability work by moving from a vertically scaled EC2 setup to a horizontally scaling Kubernetes/EKS deployment, using CI/CD to safely incorporate requirement changes like tabular document data.”

“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”