Vetted pytest Professionals

Pre-screened and vetted.

OM

Mid-level Software QA Engineer specializing in web, mobile, and test automation

BreezeBell, WA6y exp
BreezeBellCherkasy State Technological University
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HC

Mid-Level Full-Stack Engineer specializing in AI platforms and multi-tenant SaaS

New York, New York2y exp
Property Gauge
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shuntoria reid - Senior AI Trainer and Data Annotator specializing in LLM and computer vision datasets in Greenwood, SC

shuntoria reid

Screened ReferencesModerate rec.

Senior AI Trainer and Data Annotator specializing in LLM and computer vision datasets

Greenwood, SC11y exp
RWSFull Sail University

Early-career software engineer with a blended background in technical support, QA, data work, and hands-on web development. Built QA-tracker-style applications and small AI-enabled prototypes using Python, Flask, SQL, JavaScript, and React, with a strong emphasis on testing, debugging, and turning messy requirements into practical user workflows.

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Steve Louis - Mid-level QA Engineer specializing in manual testing, UAT, and API/SQL validation in Brooklyn, NY

Mid-level QA Engineer specializing in manual testing, UAT, and API/SQL validation

Brooklyn, NY9y exp
Battalion Christian AcademyWestminster Kingsway College
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MM

Junior QA Engineer / Systems Analyst specializing in test automation and ERP implementations

São Paulo, Brazil2y exp
VokeSPTech
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KA

Mid-level Software QA Engineer specializing in manual and automation testing (web, mobile, API)

Charlotte, NC4y exp
CloudberryCareerist
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Rutvi Rathod - Junior Full-Stack Software Engineer specializing in AI and web applications in San Jose, CA

Rutvi Rathod

Screened

Junior Full-Stack Software Engineer specializing in AI and web applications

San Jose, CA1y exp
FreelanceChhotubhai Gopalbhai Patel Institute of Technology

LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.

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