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Vetted MongoDB Professionals
Pre-screened and vetted.
Mid-Level Software Engineer specializing in full-stack web development and cloud DevOps
Mid-level AI/ML Engineer specializing in cloud MLOps and scalable model deployment
Mid-level Full-Stack Developer specializing in cloud-native FinTech and AI integrations
Senior Engineering Leader specializing in SaaS platform modernization and scaling teams
Senior Software Engineer specializing in cloud-native Java microservices
Mid-level Backend Software Engineer specializing in Python microservices and cloud-native APIs
Intern Full-Stack Software Engineer specializing in AI-powered web applications
Mid-level .NET Developer specializing in cloud-native web applications on Azure
Senior CRM Specialist specializing in omnichannel marketing automation
Senior Full-Stack JavaScript Developer specializing in React and Node.js
Mid-level Data Scientist / AI/ML Engineer specializing in Generative AI and healthcare analytics
Mid-level Full-Stack Java Developer specializing in cloud-native microservices
Senior Full-Stack Engineer specializing in .NET, Angular, Azure, and AI automation
Mid-level Machine Learning Engineer specializing in MLOps and applied data science
Executive IT Leader / Deputy CIO specializing in enterprise architecture, cybersecurity, and cloud transformation
Mid-level Full-Stack Software Engineer specializing in GenAI and SaaS platforms
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Dhruvil Chodvadiya
Mid-level Full-Stack Developer specializing in FinTech, Healthcare IT, and Generative AI
“Full-stack + ML engineer who built “Finsight,” a real-time financial risk platform (React/FastAPI/MongoDB/AWS Lambda) processing 2M+ records monthly, using sharding and Redis caching (60% DB load reduction) plus async and batch optimizations. Also has healthcare product experience at Apollo Healthcare, partnering directly with clinicians/admins to design and iterate EHR dashboards via Figma prototyping and user testing, and demonstrates clear system design thinking for real-time voice-to-LLM architectures.”