Vetted Amazon SQS Professionals

“Full-stack engineer who has delivered production customer analytics/dashboard features using Next.js App Router + TypeScript on the frontend and Java Spring Boot microservices on the backend. Demonstrates strong production ownership (monitoring latency/error rates/adoption) plus hands-on performance work across React rendering and Postgres query/index optimization, and has implemented Temporal-like durable workflows with retries and idempotency.”

“Forward Deployed Engineer at EasyBee AI who productionized a self-storage customer’s multi-agent LLM system end-to-end—rebuilding it with LangGraph/CrewAI, integrating with real property management + CRM systems via an MCP server, and adding observability/guardrails for reliable daily use. Experienced in live troubleshooting of agentic workflows, developer demos/workshops (including an open-source project, MerryQuery), and partnering with sales to close deals through customer-specific technical demos and fast integration feedback loops.”

“Recent research master’s graduate who built a Python backend for an interconnected RabbitMQ/MQTT system, including automation, benchmarking, and network-link optimization between brokers. Has hands-on edge computing experience (lightweight Kubernetes on a ~40 Raspberry Pi IoT cluster) and made architecture decisions for real-time streaming by evaluating Kafka and ultimately choosing Apache Storm.”

“Full-stack engineer with fintech payments experience who shipped an end-to-end guest invoice payment flow emphasizing reliability under retries/failures (idempotency via DynamoDB, async processing with Lambda/EventBridge/SQS + DLQ). Also built a FastAPI backend with Cognito/JWT + scoped guest tokens and a polished React/TypeScript checkout UX, and has performance-focused Postgres/Redis design experience for flash-sale e-commerce workloads.”

“Startup-style full-stack/backend engineer with hands-on AWS architecture experience who shipped an LLM-driven assessment-question automation feature (Python microservice calling AWS Bedrock via SQS, deployed on Lambda) with strong validation/guardrails and retry strategies. Also improved production scalability by moving a CPU/IO-heavy file upload path out of a Go API into a queue/Lambda design monitored with CloudWatch, and has React+TypeScript experience optimizing analytics dashboards.”

“Backend/AI engineer in construction tech (HyperWater AI) who delivered major production performance wins (analytics API from ~1 hour to 0.5s) and shipped LLM features for parsing subcontractor manifests into CSI divisions with human-in-the-loop review. Also built a freelance agentic document-verification system using OCR + RAG over pgvector with robust retry/escalation logic and user feedback loops.”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Software/product engineer who owns customer-facing internal platforms end-to-end, with deep experience building data pipeline health and data quality tooling (near-real-time alerting and ops dashboards). Strong in React/TypeScript + Python REST architectures and microservices with RabbitMQ, emphasizing reliability patterns (idempotency, DLQs, correlation IDs) and fast, safe iteration via feature flags, testing, and observability.”