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Vetted Microsoft Azure Professionals
Pre-screened and vetted.
Senior Software Engineer / DevOps specializing in cloud-native distributed systems
Mid-level Software Engineer specializing in FinTech and scalable backend systems
Senior .NET Full-Stack Developer specializing in cloud-native microservices
Director-level Mobile & Full-Stack Software Engineer specializing in Android and cloud-native apps
Mid-level QA Automation Engineer specializing in healthcare test automation and DevOps
Director-level Product Executive specializing in SaaS, AI, and platform modernization
Mid-level Full-Stack Java Developer specializing in cloud-native microservices
Senior Full-Stack Engineer specializing in Python web platforms and cloud systems
Senior Full-Stack Engineer specializing in backend systems and cloud-native platforms
Mid-Level Full-Stack Java Developer specializing in Spring Boot, React, and AWS
Junior Software Engineer specializing in backend systems, QA automation, and AI/ML
Executive Engineering Leader specializing in Product, Mobile, and SaaS platforms
Staff Engineer specializing in applied AI and healthcare platforms
Executive CTO and venture builder specializing in AI-native SaaS and consulting
Chathrapathi Nikhil Kandagatla
Mid-level Full-Stack Software Engineer specializing in React/Node and cloud-native web apps
“Full-stack engineer who built and iterated a CRM dashboard at ReplyQuick by sitting with end users, prioritizing blockers, and shipping frequent updates—improving usability and performance enough to replace a spreadsheet workflow within ~2 months. Demonstrates strong security fundamentals (OAuth2/JWT + RBAC) and practical microservices experience (decoupling a CRM API from a PDF-processing service via async processing and status tracking).”
Sravani Gangaraju
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”