Pre-screened and vetted.
Mid-level Frontend Developer specializing in React, Next.js, and TypeScript
Junior QA Engineer specializing in console, VR, and web/mobile testing
Mid-level Software QA Engineer specializing in web, mobile, and API testing
Mid-level QA Specialist specializing in web, API, and test automation
Intern Software Engineer specializing in AI, computer vision, and VR
“Robotics software engineer with hands-on experience integrating vision-based perception with control logic in ROS simulation environments. Focused on debugging real-time timing/data-flow issues, improving system stability through incremental scenario testing in Gazebo, and supporting reliable deployments with Docker and basic CI/CD automation.”
Junior Front-End Developer and Mass Communication graduate
“Frontend engineer who has led end-to-end builds and major feature launches in fast-paced fintech and logistics environments. Experienced designing scalable React/TypeScript architectures (Atomic Design, Redux Toolkit, React Query) and shipping reliably with CI/CD, comprehensive testing (Jest/Cypress), feature-flagged rollouts, and performance-focused UX patterns like optimistic updates and skeleton loading.”
Junior .NET Developer specializing in ASP.NET MVC and test automation
“Unity gameplay programmer focused on player movement and "game feel," implementing responsive controls (variable jump height, forgiveness mechanics) by combining Update-based input with FixedUpdate physics and velocity-driven movement. Brings client-server/REST fundamentals from outside games and is actively ramping into Unity multiplayer (Photon/Mirror-style) via small synchronization prototypes.”
Entry-Level Software QA Specialist specializing in manual and automated testing
Junior QA Engineer specializing in ERP, API, and performance testing
“Built an automated ML/NLP document classification system for unstructured legal documents, combining classical models (TF-IDF + logistic regression/random forest) with entity resolution via fuzzy matching validated by precision/recall. Also implemented semantic similarity search using sentence embeddings stored in FAISS and improved matching by fine-tuning a transformer on domain-specific data and tuning similarity thresholds for fewer false positives.”