Vetted Retrieval-Augmented Generation (RAG) Professionals

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“Machine learning/NLP engineer with deep Azure stack experience (Data Factory, Databricks/Spark, Delta Lake, Azure OpenAI, Azure AI Search) who built end-to-end production systems for semantic clustering, entity resolution, and hybrid search. Demonstrated measurable gains from embedding fine-tuning (~15% retrieval precision, ~10–12% nDCG@10) and designed scalable, quality-checked pipelines with MLOps best practices.”

“Robotics software engineer focused on hard real-time autonomy for legged robots, building a quadruped navigation stack that combines vision SLAM with MPC and maintains a deterministic 500Hz control loop. Deep performance optimization experience across CUDA (sub-2ms perception latency), ROS 2/DDS real-time tuning, and motion planning (cut 500ms spikes to sub-5ms). Also designed distributed ROS 2 + Zenoh communications between quadrupeds and aerial drones and validated robustness under lossy wireless conditions.”

“LLM/agentic systems builder who shipped production workflows at Angel Flight West and Eureka AI, combining LangGraph + RAG (Postgres/pgvector) with strong observability (LangSmith/Langfuse). Delivered large operational gains (address lookup cut from 10 minutes to 60 seconds; accuracy to 92%) and has a track record of quickly stabilizing customer-critical pipelines (Pydantic-enforced JSON for ETL) while partnering with sales/ops to drive adoption.”

“AI Automation Engineer at BalancedTrust who single-handedly shipped production LLM features for FinTech compliance: a policy gap-analysis pipeline (SOC 2/GDPR) and a RAG-based regulatory chatbot. Deeply focused on reliability in high-stakes legal/compliance settings, with strong production engineering (edge functions, parallelized batching to cut latency, structured JSON outputs, guardrails, and monitoring) and close collaboration with non-technical compliance experts.”

“Built and shipped a production LLM-powered financial Q&A interface that extracts precise numeric data from PDFs using a hybrid AWS Textract + LLM normalization pipeline, with confidence gating and guardrails to prevent unreliable answers. Experienced with LangChain-based RAG orchestration (chunking, memory, structured outputs) and collaborated closely with PMs/analysts on IRS Form 990 extraction requirements.”

“Hands-on engineer focused on productionizing LLM-powered assistants: builds RAG pipelines with guardrails, response schemas, and citation-grounded outputs, then hardens them with explicit NFRs (latency, uptime, security, cost). Experienced diagnosing agentic/LLM workflow issues in real time using observability and stepwise isolation, and supports go-to-market via developer demos, workshops, and pre-sales technical evaluations in microservices/Spring Boot environments.”

“Backend engineer who built an AI RAG quoting system for the fastener industry, reducing quote turnaround from weeks to ~30 minutes and raising retrieval accuracy to ~90% by solving a semantic-collision issue with a parent-document retrieval design. Strong in production AWS integrations (Cognito auth, S3 pre-signed uploads), performance optimization (multithreading/out-of-core), and real-time streaming (Kafka/Spark Kappa architecture achieving sub-second latency), plus Kubernetes logging and GitHub Actions CI/CD to ECR.”

“Built and deployed a production LLM-powered email assistant (“wood flow”) for a local pet resort to automate after-hours inbound email handling, including email categorization and context-aware auto-responses. Uses n8n for orchestration and applies CRISP-DM, load/edge-case testing, and RAG-based context retrieval, and has experience presenting AI solutions with budgeting and ROI to a non-technical founder.”

“Healthcare data/NLP practitioner with experience at UnitedHealthcare building production ML systems that connect unstructured call center transcripts and medical notes to structured claims data. Has delivered measurable impact (25% classification accuracy lift; ~30% relevance improvement) using classical NLP, embeddings (Sentence-BERT + FAISS), and AWS SageMaker deployments with robust validation and drift monitoring.”

“Built and shipped Serrano AI, a multi-tenant SaaS conversational AI platform that automates Odoo ERP workflows and lets ops/finance/supply-chain teams query ERP data in natural language. Implemented a multi-agent architecture (LangChain/LangGraph/CrewAI) with hybrid RAG over ERP schemas, deployed on Heroku/Vercel with production observability, cutting reporting time by ~80% while addressing hallucinations, latency, and schema complexity.”

“Built Hexagon’s production Text-to-CAD Copilot that converts text and rough sketches into editable CAD code, combining GraphRAG (Neo4j/LangChain) with a Gemini-powered vision module and multi-agent geometric validation—cutting manual modeling from a day to ~45 seconds and driving retrieval latency below 50ms. Also has large-scale GCP data/ML orchestration experience (Airflow/Cloud Composer, Dataflow, Pub/Sub, Snowflake) processing 50M+ daily records with drift monitoring and automated reliability controls.”

“Defense-domain AI engineer who built a production ReAct-style RAG system for military training data/material generation, scaling to ~1000 users and cutting generation time by 50%. Also has experience designing GPU-cluster parallel computation with PyTorch and handling production incidents involving database performance and schema design.”

“Full-stack engineer who built and shipped an AI-powered internal knowledge search system for APL Services, including document ingestion into a vector database, a Python backend, and a React/TypeScript chat-style UI with source citations for trust. Improved production reliability by migrating from Streamlit Cloud to GCP with containerization and scaling controls to eliminate cold-start friction; also co-led a Mensa chapter website redesign as Digital Communications Committee co-chair.”

“Backend/infrastructure-focused engineer building AI-agent products for small businesses, including a customer-service agent platform with intent routing, RAG over Pinecone, and external booking API integration. Has shipped Python/FastAPI services with JWT auth, versioned APIs, Docker deployments to AWS EC2 via GitHub Actions, and production monitoring with Prometheus/Grafana.”

“Data professional with ~4 years of experience, most recently at AIG (insurance), building ML/NLP systems for fraud detection and policy automation using transformers, CNNs, and clustering/anomaly detection. Also developed a RAG-based knowledge retrieval system, iterating across embedding models and moving to production based on precision and latency SLAs, then containerizing and deploying with SageMaker and CI/CD.”

“Backend engineer with airline operations domain experience who modernized flight-ops systems from batch updates to real-time streaming on AWS (Kafka + Spring Boot microservices), improving latency and stability through metric-driven tuning and idempotency. Also shipped a production LLM decision-support component using RAG over operational logs and internal procedures, with strong guardrails and an evaluation/regression loop to reduce hallucinations and enforce grounding.”

“Built and productionized a RAG-based LLM research assistant for biomedical and regulatory document search using Mixtral 7B on SageMaker, LangChain, and Milvus, cutting research time by ~40%. Has hands-on multi-cloud MLOps experience across AWS/Azure/GCP with Kubeflow/Airflow/Composer plus Terraform + ArgoCD, and applies rigorous evaluation/monitoring (latency, accuracy, hallucinations). Also partnered with a non-technical PM to deliver an insurance policy Q&A chatbot that reduced customer response time by 30%+.”