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nihale_nya

Oumaima M

@nihale_nya

AI Engineer and Software Developer, Multi Agent Systems,RAG,Automation

Marruecos
Inglés, Francés, Árabe
Parte de la información aparece en idioma inglés.
Sobre mí
Hi! I'm Oumaima, a final-year Computer Science engineering student and AI Engineer with hands-on experience building real-world AI solutions. I’ve worked on multi-agent systems using Google ADK, built RAG pipelines with LightRAG, developed ML models, and delivered automation workflows using Microsoft Power Automate and Copilot Studio. I love turning complex problems into practical solutions — whether it’s an AI agent, a custom chatbot, a data-powered application, or a full automation pipeline. My experience includes winning the AI Builders Challenge 2024 (1st place), working on VR training ap... Lee más

Habilidades

n
nihale_nya
Oumaima M
desconectado • 

Revisa mis servicios

Integraciones de IA
I will help businesses save time by automating work with ai
DeUSD 90 / proyectoMás información
Aplicaciones GPT personalizadas
I will build ai agents and rag systems for your business data
DeUSD 40 / proyectoMás información

Experiencia laboral

AI engineer Intern

Futurate by Infosysta • Tiempo parcial

Jun 2025 - Sep 2025 • 3 mos

I contributed to the development of an AI-driven solution designed to automate the analysis of complex RFP (Request for Proposal) documents and accelerate technical report generation. The primary business objective was to reduce manual workload, shorten response times, and improve consistency in proposal analysis for enterprise and consulting teams. The solution enabled faster identification of key requirements, deadlines, and deliverables from large, unstructured documents. By working closely with product and business stakeholders, I helped translate operational needs into practical AI workflows, continuously refining the system based on real user and partner feedback. The outcome supported more efficient decision-making, increased productivity, and improved competitiveness in high-pressure bidding environments. While the system relied on a multi-agent AI architecture and a domain-specific knowledge base, the focus remained on delivering scalable, reliable business value rather than purely technical complexity