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Vetted Redis Professionals
Pre-screened and vetted.
Maulik Maganbhai Ghatala
Entry Backend Software Engineer specializing in Python/FastAPI and cloud-native APIs
“Backend engineer who built and evolved a low-latency document search platform (C++/gRPC on Kubernetes with a vector database), emphasizing resilience under concurrent load through strict deadlines, retries, idempotency, and observability. Also experienced building secure, frontend-friendly FastAPI services (Pydantic + JWT) and executing safe incremental refactors using feature flags and parallel validation.”
Minh Huynh
Junior AI/ML Engineer specializing in LLM systems and personalization
“Backend engineer who built and scaled AmazonProAI, a multi-tenant SaaS platform for Amazon sellers, using a modular Django/DRF monolith with strict seller-level isolation and security controls. Led a controlled SQLite-to-PostgreSQL migration and hardened bulk Excel ingestion with idempotency and data integrity constraints to prevent duplicate metrics and noisy alerts while keeping the system ready for future service extraction.”
Junior Software QA Engineer specializing in mobile and web application testing
Entry-level Software Engineer specializing in game development and backend systems
Entry-Level Full-Stack Software Engineer specializing in backend systems and cloud deployment
Zackary Liel
Junior Software Engineer specializing in full-stack and systems development
“Backend-focused developer who built LinguaTile (language learning app) on a FastAPI + MongoDB monolith deployed to Google Cloud Run, emphasizing async performance and security (RBAC/JWT, rate limiting, request tracing). Also created Mark-RS, a static HTML generator with a 100% CommonMark-compliant Markdown parser, demonstrating strong edge-case rigor and systems robustness.”
Rutvi Rathod
Junior Full-Stack Software Engineer specializing in AI and web applications
“LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.”
Entry-level Full-Stack Software Engineer specializing in web and real-time applications
Naman Limani
“Built multiple AI projects end-to-end as a solo developer, including a privacy-focused LLM app that redacts PII before sending prompts to an external model and a LangGraph-based multi-agent triage system for log analysis. Stands out for combining LLM/agent design, deployment troubleshooting, and practical workflow automation with a strong emphasis on privacy and explainability.”