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Vetted Amazon CloudWatch Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in Python/Flask, React, and autonomous AI systems
Mid-level Java Full-Stack Developer specializing in cloud microservices and AI/ML integration
Mid-level DevOps/Cloud Engineer specializing in AWS EKS and Terraform
Entry-Level DevOps Engineer specializing in CI/CD automation and cloud infrastructure
Mid-level Full-Stack Software Engineer specializing in Java/Spring Boot, React, and AWS
Mid-level DevOps & Customer Success Engineer specializing in cloud, networking, and GenAI
Mid-level Data Engineer specializing in cloud-native batch and streaming pipelines
Mid-level DevSecOps Engineer specializing in AWS cloud security
Lalith Kambala
Junior Full-Stack Engineer specializing in AI-powered web applications
“Full-stack product engineer who has shipped AI-powered job board moderation and validation features end to end across React/TypeScript, serverless backends, and Postgres. Stands out for combining UX polish, LLM-backed workflow design, and reusable async infrastructure patterns to improve reliability, speed of delivery, and user participation.”
Mid-Level Software Engineer specializing in Java/Spring microservices and cloud
Senior Full-Stack AWS Developer specializing in cloud-native microservices and serverless systems
Megha John Babu
Junior Full-Stack Software Engineer specializing in scalable backend systems
“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.”
kireeti Sangarsh
Mid-level Data Engineer specializing in cloud ELT pipelines and analytics engineering
“Data engineer who has owned end-to-end ELT pipelines on Airflow + AWS (S3/Glue/Lambda) with Snowflake/Redshift, processing millions of records per day and tens of GBs via PySpark. Built strong data quality and reliability practices (40% quality improvement, 99%+ uptime), and also designed a resilient web-scraping system with anti-bot defenses and schema-change versioning plus REST APIs for serving curated data.”
Tejaswi devarapalli
Junior Software Engineer specializing in backend systems and AI infrastructure
“Built both a full-stack AWS file-processing pipeline and a production AI document Q&A system ('smart-doc'). Stands out for combining strong cloud engineering with practical LLM/RAG architecture, including hybrid retrieval, reranking, structured outputs, confidence-based retries, and production monitoring.”