Vetted AWS Step Functions Professionals

Pre-screened and vetted.

DT

Junior Full-Stack AI Developer specializing in multi-agent LLM systems on AWS

Alamo, CA2y exp
DAVTEQSan Francisco State University
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DG

Intern AI/ML Engineer specializing in NLP, graph analytics, and agentic RAG systems

Dallas, TX2y exp
FlashmockUniversity of North Texas
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ML

Senior Machine Learning Engineer specializing in Generative AI and MLOps

Stafford, VA10y exp
DenebSolution
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JA

Staff/Lead DevOps & Site Reliability Engineer specializing in cloud infrastructure and Kubernetes

Rosenberg, TX12y exp
IT GOAT
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SH

Senior Full-Stack AWS Developer specializing in cloud-native microservices and serverless systems

Irving, TX4y exp
Chicago Education Advocacy CooperativeSathyabama Institute of Science and Technology
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OP

Junior Software Engineer specializing in backend and full-stack development

Remote1y exp
Affordable Robotic & Automation LtdCleveland State University
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RZ

Ricky Zheng

Screened

Senior Backend/AI Engineer specializing in AWS-native data processing and legacy modernization

Rancho Cucamonga, CA14y exp
NEQTO AIPasadena City College

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.

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PP

Entry-level Software Engineer specializing in AI/ML and cloud backend systems

United States1y exp
Careyou PharmacyUniversity of Wisconsin-Parkside
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RM

Mid-level Python Developer specializing in cloud-native APIs and microservices

Plymouth, MN5y exp
Tekpro IT SolutionsSacred Heart University
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Built and deployed an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.

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