Vetted Amazon CloudWatch Professionals

Collin Brennan

Screened

Entry Software Engineer specializing in full-stack web development and AI search

1y exp

“Frontend-leaning full-stack product engineer who recently migrated their team's frontend to TypeScript and built a cross-system Mentions feature integrating Gmail and other external apps. Strong on practical React architecture, performance debugging, and shipping fast with thoughtful tradeoffs around caching, API limits, and real-time UX.”

“Hands-on automation and QA-focused developer using AI agents, MCP tools, and LLMs to streamline business workflows. Built agents for automated Jira bug logging, executive summary dashboards, and a rule-explainer that translates technical business rules into plain language for end users, while also supporting Selenium-to-Playwright migration and guiding peers on AI implementation.”

“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.”