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Vetted Amazon SQS Professionals
Pre-screened and vetted.
Senior Full-Stack AI Engineer specializing in cloud-native SaaS platforms
Senior Software Engineer specializing in backend systems and high-throughput data pipelines
Staff Full-Stack & AI Engineer specializing in LLM agents, RAG, and cloud-native systems
Junior Software Engineer specializing in full-stack and machine learning systems
Mid-Level Software Engineer specializing in FinTech, Treasury, and Compliance Systems
Mid-level Full-Stack & AI Backend Engineer specializing in LLM/RAG systems
Senior Software Engineer specializing in GenAI, RAG, and multi-tenant SaaS platforms
Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices
Mid-Level Software Development Engineer specializing in AWS serverless, security, and ML platforms
Mid-level Software Engineer specializing in distributed systems and infrastructure reliability
Mid-Level Software Engineer specializing in FinTech, treasury systems, and real-time data pipelines
Senior Software Engineer specializing in Python and AWS cloud-native backend systems
Mid-level Software Development Engineer specializing in AWS incident management and AI analytics
Mid-level Full-Stack Developer specializing in AI and FinTech platforms
Junior Software Engineer specializing in backend systems and cloud messaging
Executive Engineering Leader specializing in AI automation and cloud platforms
Senior Full-Stack Engineer specializing in backend systems and cloud-native microservices
Mid-Level Software Development Engineer specializing in AWS serverless and backend APIs
Entry-Level Software Engineer specializing in backend systems and cloud messaging
Shuju Sun
Mid-Level Software Engineer specializing in real-time data pipelines and ML deployment
“Ticketmaster data engineer who built CDC-driven Kafka pipelines feeding Snowflake for analytics and data science teams. Hands-on in production operations—scaled Kafka during sudden playoff-driven transaction spikes and improved monitoring for preemptive scaling. Known for using small-batch experiments and quantitative metrics to align stakeholders and drive cost-saving architecture changes (e.g., buffering to reduce AWS Lambda invocation frequency).”