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Vetted Material UI Professionals
Pre-screened and vetted.
Mid-Level Full-Stack Software Developer specializing in cloud-native microservices
Mid-level Full-Stack Engineer specializing in React, Node.js, and microservices
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices
Senior Full-Stack Engineer specializing in cloud-native microservices and AI solutions
Senior Full-Stack Software Engineer specializing in React, React Native, and Spring Boot
Senior Full-Stack Engineer specializing in React, Java/Spring Boot, and Python microservices
Senior Software Engineer specializing in GenAI and full-stack enterprise applications
Mid-level Full-Stack Engineer specializing in FinTech and cloud-native applications
Mid-level Full-Stack Developer specializing in FinTech and Healthcare IT
Mid-level Frontend Engineer specializing in React and web applications
Senior Java Full-Stack Developer specializing in insurance and healthcare platforms
Senior Frontend Engineer specializing in scalable web platforms
Senior Full-Stack Developer specializing in cloud-native FinTech microservices and React
Senior Full-Stack Engineer specializing in scalable web apps and API architecture
Mohana Satyanarayana Moganti
Mid-Level Software Engineer specializing in Cloud Infrastructure and Full-Stack Platforms
“Built and shipped a production LLM-powered grading platform that automates rubric-aligned scoring and feedback, with strong guardrails (RAG grounding, structured JSON, validation/retries) and operational rigor (metrics, drift monitoring). Experienced using CrewAI to orchestrate multi-agent workflows end-to-end and validating quality via gold-set benchmarking against human graders with regression testing on every prompt/model change.”
RAJ KUMAR
Mid-level Full-Stack Software Engineer specializing in FinTech and cloud-native microservices
“Backend engineer at Discover who built and scaled Python/Flask services for a card dispute resolution platform, tackling long-running external network validations with Celery+Redis and delivering measurable gains (response time ~3s to <300ms; throughput +40%). Experienced in high-scale PostgreSQL/SQLAlchemy optimization (partitioning, read replicas, N+1 avoidance), event-driven systems with Kafka, and integrating ML fraud detection using AWS SageMaker/Lambda/ECS with clear separation of real-time vs batch processing.”