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Vetted C++ Professionals
Pre-screened and vetted.
Mid-level Software Engineer specializing in full-stack systems and AI applications
Mid-level Software Engineer specializing in cloud-native and AI-driven enterprise systems
Entry-Level Machine Learning & GenAI Engineer specializing in RAG and LLM evaluation
Mid-level Software Engineer specializing in Python, GenAI/ML, and cloud-native systems
Mid-level Software Engineer & PhD Researcher specializing in mobile app bug localization and LLMs
QA Supervisor and Software QA Lead specializing in game, mobile, and web testing
Mid-level Software QA Engineer specializing in test automation and feature validation
Mid-Level Full-Stack Software Engineer specializing in cloud-native SaaS and automation
Mid-level Full-Stack Developer specializing in Java/Spring Boot, Python, and React
Junior Computer Engineering student specializing in software, embedded systems, and IT support
Entry-Level Full-Stack Software Engineer specializing in AI/ML and web applications
Junior Software Developer specializing in full-stack web and cloud systems
Mid-level Full-Stack Developer specializing in cloud-native microservices and data pipelines
Senior Software Engineer specializing in cloud-native distributed systems and ML/LLM infrastructure
Mid-level Machine Learning Engineer specializing in agentic AI, computer vision, and NLP
Junior Software Engineer specializing in cloud-native APIs and machine learning
Junior Robotics & Machine Learning Engineer specializing in perception, SLAM, and control
Mid-level Game QA & Narrative Analyst specializing in gameplay, performance, and storytelling
Junior Software Engineer specializing in Python backend, data pipelines, and real-time systems
Mid-Level Full-Stack & AI Engineer specializing in GenAI and cloud platforms
Mid-level Data Analyst and AI Engineer specializing in NLP, RAG, and BI analytics
Senior Software Engineer specializing in cloud-native microservices and distributed systems
Junior Full-Stack Software Engineer specializing in cloud platforms and RAG/LLM evaluation