Vetted Amazon CloudWatch Professionals

“Built and shipped an AI-powered journaling app feature that analyzes user text for emotions in real time, owning everything from React/TypeScript UX (dynamic theming + Framer Motion) to a FastAPI backend integrating Hugging Face. Has hands-on production experience deploying Dockerized services on AWS (EC2/RDS/S3/CloudWatch) with GitHub Actions CI/CD, and resolved a real latency/scaling incident by converting blocking external API calls to async with retries/timeouts.”

“Sole software engineer for an ecommerce operation who ships and operates full-stack products end-to-end. Built a production AI voice agent integrated with Shopify and internal systems that now handles 4,000+ customer calls/month, plus a branded mobile app credited with $4M+ in sales and a self-serve returns/warehouse platform with strong React/TS UX and robust AWS operations.”

“Full-stack engineer with experience delivering an end-to-end NIH application using Angular and scalable .NET Core microservices backed by MySQL. Has hands-on depth in complex approval/workflow implementations (RBAC + workflow engine) and performance tuning for million-record, data-driven systems, plus 5 years with Java/Spring Boot microservices and React.”

“Application security engineer/advisor with hands-on experience securing AWS-based, containerized services and embedding SAST/DAST/SCA and container scanning into GitHub/GitLab CI/CD. Drove measurable outcomes (50% faster vuln triage, 40% fewer misconfigs) and has deep operational troubleshooting experience in Kubernetes (agent failures due to CPU throttling/network policies), plus pragmatic strategies to reduce developer friction and handle API rate limits.”

“Backend/ML engineer with deep fintech and marketplace experience: built a real-time financial analytics + algorithmic trading platform (Python/Postgres/Kafka/Redis) and drove major DB performance wins (10x faster analytics; sub-10ms response consistency). Also shipped an end-to-end ML recruitment matching platform (scraping/ETL/modeling/Django deployment) with reported 92% matching accuracy, and emphasizes production reliability via monitoring, blue-green deploys, and robust workflow error handling.”

“Backend/data engineer with hands-on production experience building a FastAPI Python service on AWS for real-time AI workflows (Postgres/Redis, containers behind API Gateway) with strong reliability practices (JWT auth, timeouts/retries, health checks). Has delivered AWS infrastructure using Terraform + GitHub Actions across environments, built Glue ETL pipelines into Snowflake with idempotent recovery, and modernized legacy batch workflows via parallel-run parity validation and phased cutovers.”