Vetted Retrieval-Augmented Generation Professionals

“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 an LLM-based financial document analysis system using a RAG pipeline, including robust ingestion/chunking/embedding workflows, vector DB retrieval, and an AWS-deployed FastAPI service containerized with Docker. Demonstrates strong applied expertise in improving retrieval quality and latency at scale, plus hands-on experience debugging agentic/LLM workflows with monitoring and trace-based analysis while supporting demos and customer-facing adoption.”

“Full-stack TypeScript/Angular/Node engineer who owned a production healthcare application for a pharmaceutical client, supporting 100K+ monthly users across 10+ countries. Strong focus on maintainability and quality (reusable localized component library, ~90% unit test coverage, SonarQube in CI/CD) plus performance work (reported 15% client-side latency reduction and up to 50% backend latency reduction) while migrating legacy mobile code with strict backward compatibility.”

“Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.”

“Early-career cloud/appsec-focused engineer with hands-on experience building secure, observable microservice systems on AWS (IAM least privilege, KMS encryption, Secrets Manager, CloudWatch, ALB) and troubleshooting autoscaling-related 500s down to connection pooling issues. Also deployed heavy ML workloads on Kubernetes by decomposing diffusion/transformer services, using workload identity to eliminate static credentials, and maintaining GitOps-style deployment audit trails.”

“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%+.”