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Vetted Software Development Engineers
Pre-screened and vetted.
Mid-Level Front-End Engineer specializing in React, Angular, and CMS-driven sites
Intern AI Software Engineer specializing in backend systems and data platforms
Mid-level AI Engineer specializing in LLMs, RAG, and cloud-native MLOps
Mid-level AI Application Engineer specializing in GenAI (RAG) and full-stack systems
Mid-level Full-Stack Developer specializing in Spring Boot microservices and React
Mid-Level Full-Stack Software Engineer specializing in cloud, mobile, and GenAI
Mid-Level Software Engineer specializing in full-stack web and backend systems
Mid-level Software Engineer specializing in cloud data platforms and microservices
Mid-Level Full-Stack Software Engineer specializing in scalable web apps and FinTech systems
Mid-level Full-Stack Software Engineer specializing in Java microservices and cloud platforms
Mid-Level Software Engineer specializing in Java microservices and cloud-native development
Mid-Level Full-Stack Software Engineer specializing in cloud-native web apps and PDF/canvas tooling
Mid-level Data Engineer and AI Engineer specializing in LLMs and data platforms
Junior Software Engineer specializing in backend systems, AI agents, and EdTech platforms
Mid-Level Full-Stack Software Engineer specializing in scalable systems and GenAI
Mid-Level Full-Stack Software Engineer specializing in SaaS logistics and cloud-native systems
Junior Machine Learning Engineer specializing in LLM agents, knowledge graphs, and multimodal AI
Mid-Level Software Engineer specializing in platform reliability and microservices
Swarag Reddy Pingili
Junior AI/ML Software Engineer specializing in LLM agents and RAG systems
“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”