a
amnaikram360

Amna Ikram

@amnaikram360
5,0(1)

Full Stack Developer, AI SaaS Developer, MVP Specialist, RAG, AI Agents

Paquistão
Inglês
Algumas informações são exibidas no idioma inglês.
Sobre mim
I build production-ready full-stack SaaS platforms and deliver MVPs in 4–6 weeks. I’m Amna Ikram, specializing in SaaS development, AI integration, and scalable web apps for startups. My stack includes React, Next.js, TypeScript, Python, Node.js, PostgreSQL, and MongoDB. I work with APIs, WebSockets, and LLM integrations like OpenAI and RAG. I also handle DevOps (Docker, CI/CD, AWS) and security (JWT/OAuth). Message me with your idea, I’ll outline your MVP within 24 hours.... Saiba mais

Habilidades

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amnaikram360
Amna Ikram
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Tempo médio de resposta: 1 hora

Conheça meus serviços

Software e Sites de IA
I will build ai powered mvps and web applications using gpt, claude, and more
Desenvolvimento e MVP
I will build your website or web app using ai vibe coding with claude code

Portfólio

Experiência profissional

Full Stack Developer

Nexus • Meio período

May 2023 - Present3 yrs

Developed a comprehensive Full-Stack Development SaaS MVP using React for the frontend and Flask for the backend, focusing on providing financial professionals with tools for real-time data analysis. 🔷 Frontend Development: Crafted responsive user interfaces using React and Chakra UI, ensuring optimal user experience across devices. 🔷 Backend Collaboration: Worked closely with a backend developer, contributing to specific portions of the server-side code using Flask. Integrated MongoDB for streamlined data management and operations. 🔷 AI Integration: Incorporated the GPT-4 API to facilitate a GPT-like chatbot, enabling clients to inquire about their specific use cases directly on the platform. 🔷 Security Protocols: Implemented JWT-based authentication and established role-based authorization mechanisms, fortifying platform security and user data protection. 🔷 Performance Management: Continuously monitored and optimized website performance, achieving a 30% reduction in load times

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