Vetted Amazon SQS Professionals

Paul Liu

Screened

Senior Software Engineer specializing in cloud-native backend and web/mobile apps

Seattle, WA8y exp

“Backend engineer with Tesla experience building Python-based, serverless microservices for a supply chain portal, including a MongoDB-backed tracking/logging system and a reconciler Lambda to manage retries and failures. Has hands-on Kubernetes (EKS) and GitOps (Argo CD) experience, plus real-time Kafka pipelines for fleet/IoT telemetry and proxy-based migrations from monolith systems to AWS databases.”

Deekshit Myakala

Screened

Mid-level Software Engineer specializing in cloud automation and data/ETL platforms

Arlington, Virginia6y exp

“Backend engineer with AWS multi-region production experience building APIs and workflow automation for data center/storage hardware operations (firmware orchestration, maintenance checks, ticketing, dashboards). Also shipped an internal AI chat tool that parses hardware runbooks and incorporates user feedback to retrain the model, and has a strong testing/quality discipline (95%+ coverage) plus database performance tuning via indexing and query monitoring.”

“Full-stack engineer with AWS Skill Builder experience building internal content-management and search workflows in TypeScript across React and Node.js. They drove a shift from keyword to semantic search using OpenSearch and Bedrock Titan Embeddings, delivering a 5x reduction in discovery time while also improving production reliability and observability for large-scale content workflows.”

Jenya Rusinova

Screened

Mid-level Frontend Engineer specializing in cloud enterprise applications

East Palo Alto, CA3y exp

“Frontend-focused product engineer with experience owning production launches in observability/monitoring, including extending an existing web-only service to support mobile users without disrupting current customers. Stands out for balancing speed and safety through feature flags, regional beta rollouts, bug bashes, and close collaboration with PM, design, and backend teams in ambiguous environments.”

“Full-stack engineer with deep serverless AWS experience who built VidToNote, an AI video analysis platform, end-to-end using Next.js App Router/TypeScript and an event-driven pipeline (API Gateway, Lambda, DynamoDB, S3, Step Functions, SQS). Strong on production reliability and observability (CloudWatch, X-Ray, structured logging), plus data/analytics work in Postgres with measurable query optimizations and durable LLM evaluation workflows. Amazon background; integrated 22 AWS services and completed AWS Solutions Architect Professional certification within a month.”

“Amazon engineer who built an Amazon Bedrock-powered summarization layer over large-scale network/service telemetry (“top talker” insights) to help security engineers triage anomalies faster. Emphasizes production-grade design patterns for LLM features—non-blocking enrichment, deterministic fallbacks, strict structured outputs, and monitoring to preserve trust in source-of-truth telemetry.”

“Backend/data engineer with hands-on production AWS experience spanning serverless APIs (Chalice/Lambda/API Gateway/Cognito) and data pipelines (Glue PySpark + Step Functions). Has modernized a legacy SAS reporting system into AWS microservices and implemented schema-drift detection and incident prevention for ETL workflows, plus measurable SQL tuning wins (30 min to <10 min runtime).”

Zheng Wu

Screened

Junior Software Engineer specializing in backend systems and cloud messaging

Mountain View, CA1y exp

“Data/ML engineer who has owned end-to-end systems across email deliverability/segmentation and production LLM apps. Built a Spark+Airflow segmentation engine that materially improved deliverability (99.9%) and open rates (>50%), and shipped a PDF-to-quiz RAG product using LangChain/Vertex AI/Chroma with strong guardrails and an eval loop that cut hallucinations to <5%.”

“Technical lead who shipped a production GPT-4-powered customer support agent for Square, serving a large fintech customer base through a React chat interface with tool-using orchestration, guardrails, and live handoff paths. Brings strong real-world experience in agent reliability, evaluation, observability, and workflow orchestration using Temporal, Sidekiq, Pinecone, Datadog, and Snowflake.”

“Engineer with a rare mix of Android, backend, and AI workflow automation experience, spanning Amazon Flex mobile features and legal-tech document automation. Built a globally used in-app recording pipeline at Amazon and previously helped cut immigration legal drafting time by ~50% through AI-assisted document parsing, form filling, and human-in-the-loop review.”

Shivam Gupta

Screened

Mid-level Software Engineer specializing in distributed systems, AI, and FinTech

USA5y exp

“Full-stack engineer with experience at AWS and Barclays, spanning large-scale cloud infrastructure, customer-facing product development, and production GenAI systems. Stands out for owning systems end-to-end—from backend architecture and frontend UX to AI reliability and cost optimization—with measurable impact including 25% fewer support tickets, 10% subscription growth, and debugging time reduced from 30 minutes to 5 minutes.”

Jordan Lineberry

Screened

Senior Full-Stack Engineer specializing in Healthcare SaaS and supply chain systems

New York, NY11y exp

“Backend engineer with healthcare platform experience at Thirty Madison, combining Django for secure, data-heavy core services with FastAPI async microservices for real-time patient monitoring. Led Kubernetes migration with Istio service mesh, autoscaling (HPA), and stateful storage patterns, and implemented GitOps CI/CD using ArgoCD. Also built real-time Kafka streaming pipelines with reliability patterns like idempotent producers and offset management.”

“Candidate describes a development approach centered on AI-assisted coding, testing, and agent-driven workflows, including production exposure to multi-agent systems and governance-oriented logging. They appear particularly focused on combining AI speed with structured validation through unit tests, boundary tests, and edge-case monitoring.”