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Vetted Unit Testing Professionals
Pre-screened and vetted.
Senior Full-Stack Developer specializing in Azure cloud-native microservices
Senior Backend/Infrastructure Engineer specializing in Python microservices and AWS
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent LLM systems
Mid-Level Software Developer specializing in backend systems and data engineering
Mid-Level Software Engineer specializing in cloud-native full-stack and DevOps
Junior Full-Stack Software Engineer specializing in React, Node.js, and cloud-native microservices
Junior Full-Stack Engineer specializing in AI-powered web applications
Executive engineering leader specializing in startup platforms, cloud architecture, and AI/Web3
Mid-level Machine Learning Engineer specializing in distributed AI systems
Mid-level Full-Stack Software Engineer specializing in AI-powered document platforms
Senior Full-Stack Software Engineer specializing in Python/Django and React/TypeScript
Senior Full-Stack Python Engineer specializing in secure cloud platforms and ML systems
Senior .NET Full-Stack Developer specializing in ASP.NET Core, Angular, and Azure
Senior Full-Stack Engineer specializing in Python, cloud-native microservices, and React
Mid-level QA Automation Engineer specializing in web, API, and mobile test automation
Senior Full-Stack Engineer specializing in SaaS, LegalTech, and Web3
Naveen Kancharla
Mid-level AI & Backend Engineer specializing in RAG systems and scalable APIs
“Built and deployed a production LLM-powered document Q&A system using a strict RAG pipeline (LangChain-style orchestration + FAISS) to help users query large internal document sets. Demonstrates strong reliability focus through hallucination mitigation, curated offline evaluation with grounding checks, and production monitoring (latency/fallback rates) plus stakeholder alignment via demos and business metrics.”
Kruti Mehta
Mid-level Software Engineer specializing in backend systems and FinTech analytics
“Engineer with a pragmatic, high-leverage approach to AI-assisted development: uses AI and multi-agent workflows aggressively for implementation and internal tooling, while maintaining strict human oversight for user-facing features. Stands out for treating agents like junior engineers, breaking work into actionable tasks, and combining robust testing, local E2E validation, and feature-flag rollouts to safely ship production code.”
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.”