Vetted Retrieval-Augmented Generation (RAG) Professionals

“Tech executive with Disney experience who has repeatedly scaled and restructured engineering organizations (from 4 to 30 and up to 100+), using OKRs/KPIs to drive business-aligned roadmaps. Hands-on with architecture and platform strategy, including adopting MongoDB Atlas to centralize transactional data and building shared core services (security/permissions, auditing, compliance) to increase product velocity across distributed teams.”

“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”

“Built a chatbot for a learning management system during a Deep Atlas bootcamp by mapping an end-to-end RAG architecture (document ingestion, Qdrant-based retrieval scoring, and LLM response synthesis). Previously at Rally Health/UnitedHealthcare, diagnosed load-related memory spikes with JMeter and improved stability by migrating caching from Guava to Redis, and also supported adoption through UI A/B testing in a technical marketing engineer rotation.”

“Robotics-focused engineer with ROS 2 experience who has built and debugged real-time, distributed control/orchestration systems under production-like latency and safety constraints. Led platform changes at Persona for a real-time verification orchestration system using deterministic state machines and async workers, and has hands-on experience stabilizing multi-robot navigation/SLAM behavior using rosbag, RViz, and stress testing in simulation (Gazebo).”

“Player-coach engineer/lead on a high-scale research integrity platform ("Lighthouse") that flags fraud/manipulation signals across ~3M academic manuscripts per year. Owns architecture decisions (ADRs), implements across Go/Java/React services, and introduced NLP (SciBERT embeddings + human-in-the-loop) to assess out-of-context citations while also handling production incidents with a data-consistency-first approach.”

“Hands-on AI/LLM engineer who built a RAG-based product feature end-to-end, including prompt engineering, safety guardrails, and an automated adversarial + load-testing harness. Diagnosed real production issues (null responses) via Azure logs/metrics and drove an architectural fix by separating model deployments to address token/quota limits. Also runs internal developer enablement through short theory-to-hands-on AI workshops after completing a Microsoft AI certification.”

“AI/LLM engineer with production experience at NVIDIA, where they fine-tuned and deployed a financial-services chatbot and cut latency ~50% using TensorRT + NVIDIA Triton, scaling via Docker/Kubernetes. Also has consulting experience at Accenture delivering a predictive maintenance solution for a logistics network, bridging non-technical stakeholders with actionable dashboards.”

“Built and deployed a production RAG-based semantic search and summarization system for large legal/technical document sets, owning the full backend (embeddings, vector store, chunking, prompting) and driving a reported 40–60% reduction in manual review time. Experienced with LangChain/LlamaIndex plus Airflow/Temporal-style orchestration, and applies rigorous evaluation/monitoring (A/B tests, drift detection, staged rollouts) to keep agentic systems reliable. Also partnered with a supply-chain manager at TE Connectivity to deliver an AI inventory recommendation tool projected to drive millions in value.”

“Built and deployed an AI receptionist product for field-service businesses (HVAC/electrician), including real-time Jobber scheduling integrations and Twilio-based calling. Combines hands-on customer/operator shadowing with strong production engineering (queueing to handle API limits, rigorous testing/mocking, mirrored prod environment) and cross-layer troubleshooting, driving user adoption through review/override workflows.”

“Recent internship project at Google Workspace building an LLM-driven Python backend pipeline to extract/enrich NLP features from messy customer web domains and integrate them into a Domain Feature Store for personalization and promotions. Also has hands-on Kubernetes/Docker deployment experience for a Digital Signage SaaS backend with GitHub Actions CI, plus strong streaming-systems knowledge (Kafka exactly-once, schema evolution, Flink scaling) and built an information retrieval system handling 30,000+ cases.”