Vetted AWS Professionals

“Frontend engineer with experience leading a Flutter cross-platform mobile app end-to-end, emphasizing layered architecture (UI vs service/business logic) and performance tuning (off-main-thread image processing via isolates). Also builds React + TypeScript dashboards with reusable components (TailwindCSS) and scalable state management (Context + reducers), and has handled urgent production issues with QA-verified rollouts.”

“Built a production LLM-powered interview-prep app that ingests job postings and generates tailored preparation plans. Iterated from a single generalist LLM to a multi-LLM pipeline and used RAG to ground the final chat assistant on locally stored intermediate outputs; has also experimented with n8n vs Python-coded pipelines for orchestration.”

“AI/LLM engineer who has owned end-to-end production delivery of multi-agent RAG systems on Azure (React + FastAPI + data pipelines + Terraform), including rigorous evaluation/monitoring and reliability guardrails. Shipped an AI-driven observability root-cause analysis assistant that reduced MTTR ~30%, cut alert noise ~20%, and reached ~70% adoption in the first month; also built a clinical document Q&A system with citations and compliance-oriented controls.”

“Full-stack JavaScript developer (Node + React) who built a CRM system end-to-end, including fine-grained access control (RBAC plus per-user permissions) and user activity tracking. Currently focused on performance optimization for a data-heavy web app, applying lazy loading, image compression, and pagination to dramatically reduce load times, and has implemented per-user API rate limiting to control request bursts.”

“LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.”

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”

“Built FlowPilot, an AI-powered product that generates complete importable n8n workflows from natural-language prompts using a RAG pipeline (Qdrant + LangChain) and a multi-stage agent with a scoring/repair 'Judge' loop for intent alignment. Experienced in backend architecture across Laravel/Node microservices and production AI/RAG systems, plus performance debugging from async job offloading to database index tuning after ORM migrations.”

“Backend/infrastructure engineer in the EBS org focused on global server lifecycle and fleet reliability. Led a major modernization from manual, ticket-driven recovery to centralized Python services and operator tooling with DynamoDB-backed state, strong auth/allowlisting, and CloudWatch monitoring, plus an AWS Glue/S3/SNS data pipeline to join server and hardware datasets for global operational querying and automated recovery.”