Vetted Retrieval-Augmented Generation (RAG) 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.”

“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”

Jesse Olson

Screened

Executive AI technology leader specializing in agentic AI and enterprise transformation

22y exp

“Candidate is exploring a startup in the autonomous agentic AI space and is approaching it with a disciplined founder mindset. They are evaluating market opportunity, competition, customer pain, ROI, technical feasibility, and MVP options while already speaking with potential customers across verticals to validate needs and buying processes.”

“Software development manager who led development of the open-source AI agents framework Strands (for Amazon Bedrock), which launched mid-year and is used as a primary agent-development framework at Amazon and externally. Owned the Strands SDK end-to-end, drove adoption through stakeholder management and agile execution, and led a JavaScript-to-TypeScript migration to improve type safety and maintainability.”

Kevin Wang

Screened

Senior Software Engineer specializing in cloud infrastructure and distributed systems

Adams, MA8y exp

“Amazon engineer focused on productionizing LLM-powered developer workflows, including code assistance, debugging automation, and internal AI tooling. Stands out for combining hands-on ML systems work with strong platform engineering, including an orchestration engine that reportedly saved about $10K/day and reduced a manual workflow from 12 hours to under a second.”

Christopher Sipola

Screened

Senior Machine Learning Engineer specializing in LLMs and recommendation systems

New York, NY11y exp

“ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.”

“LLM/agent engineer who built a production code-generation agent at Corvic AI that lets non-technical users query CSV/tabular data in natural language by generating and executing Python. Focused on making LLM systems reliable and scalable via schema-aware validation, sandboxed execution-feedback retries, prompt caching/embeddings, async execution, and high-throughput data processing with Polars; also partnered with Adobe product/marketing to ship brand-aligned AI content generation for email and push notifications.”

Franklin Dickinson

Screened

Senior Data Scientist specializing in product analytics and applied AI

New York, NY9y exp

“Product data scientist at Meta who also built Fogo, a personal finance app, as a solo developer with a React/FastAPI/Postgres stack and sophisticated LLM evaluation infrastructure. Particularly strong in AI backend quality systems, experimentation, and production decision-making around model cost, latency, and reliability.”

“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”

Muneendra M

Screened

Mid-level Software Engineer specializing in event-driven backend and AI-enabled systems

San Francisco, CA4y exp

“Full-stack engineer at Stripe who owned a webhook monitoring and retry platform end-to-end, spanning backend services, React dashboards, and production operations. Stands out for combining strong distributed-systems judgment with product polish, including a reported 31% improvement in webhook delivery reliability and UI improvements that reduced support burden.”