Vetted Retrieval-Augmented Generation Professionals

“Built a production "appeals co-pilot" for a healthcare claims appeals team, combining an XGBoost/logistic ranking model with a Python/LangChain RAG stack (FAISS + Mistral 7B) to surface high-probability appeal wins and speed policy-grounded drafting. Emphasizes reliability and trust: hybrid retrieval with metadata routing, citation/eval scripts, guardrails, and an explainability layer that non-technical stakeholders could understand and override.”

“ML/NLP practitioner with banking experience (M&T Bank) who has built a GPT-4 RAG system using LangChain and Pinecone to connect unstructured customer data with internal knowledge bases, improving accuracy and reducing manual lookup time by 50%+. Strong in entity resolution and productionizing scalable Python data workflows, including major performance wins by migrating bottleneck joins from Pandas to Dask.”

“Applied ML practitioner who deployed a near-real-time water-quality monitoring tool for Gwinnett County by fusing ESA satellite imagery with in-situ measurements to predict chlorophyll-A and support early warnings for harmful algal blooms. Also working on a multimodal deep-learning project combining skin lesion images with patient tabular/text data (TensorFlow, embeddings) to predict melanoma risk.”

“Full-stack TypeScript engineer with early-stage startup experience (HomePulse; sole US engineer) who ships and owns production features end-to-end—routing/state design, API contracts, caching/pagination, and post-launch monitoring/optimization. Has delivered performance-sensitive React UIs (virtualized large datasets, React Query caching, Suspense loading patterns) and built durable job-queue workflows with idempotency/retries, plus SQL Server relational modeling for internal ticketing and knowledge-retrieval workflows.”

“Infrastructure/operations engineer with hands-on production IBM Power/AIX (AIX 7.x, VIOS, HMC) and PowerHA/HACMP clustering experience, including DLPAR changes, failover testing, and incident recovery. Also delivers modern cloud DevOps work—GitHub Actions CI/CD for Docker-to-Kubernetes on AWS and Terraform-based provisioning of core AWS infrastructure (VPC/EKS/RDS/IAM) with controlled rollouts and drift checks.”

“Backend/AI engineer who has built production Spring Boot APIs on AWS (JWT auth, Redis/MySQL) and solved a real-world silent data integrity issue by implementing idempotency keys plus DB constraints/transactions. Also shipped an LLM-based document Q&A feature using a RAG pipeline with evaluation + human review, and designed multi-step agent workflows with verification, retries, and escalation guardrails.”

“AI/LLM engineer who has shipped production RAG systems (including a 250K-document compliance knowledge tool on AWS) and focuses on reliability via citations, guardrails, and rigorous evaluation (Ragas/Opik/DeepEval). Also built a LangGraph-orchestrated webcrawler agent that cut research paper extraction from hours to minutes, and collaborated with clinical teams to deliver patient volume forecasting with an optimization layer for staffing.”

“Founding/solo engineer who rebuilt an early-stage product from the ground up: Ask NETA, an AI assistant for electricians to answer complex electrical code questions. Delivered a full-stack TypeScript system (React web + React Native iOS/Android, Express API, Postgres on AWS) with CI/CD, observability, and a Vertex AI RAG pipeline, reaching 3,000 MAUs in the first month; also built a real-time distributed scoring system handling unreliable hardware data with sequencing and retries.”

“End-to-end engineer who has productionized AI automation and RAG capabilities, building full-stack systems (React/Node/Redis/Postgres + vector DB) with evaluation-driven quality gates and monitoring. Reported ~60% reduction in manual ops time and major turnaround improvements, and has experience modernizing legacy systems safely via feature flags and parallel runs while working across product, data, and ops teams (System1).”