s
simonsayed

Saidul

@simonsayed

Full Stack AI Engineer LangChain RAG GPT4 NextJS FastAPI

Noruega
Hindi, Bengali, Inglês
Algumas informações são exibidas no idioma inglês.
Sobre mim
I am a Full Stack AI Engineer with M.Sc. from Norway and 4+ yrs shipping production systems. AWS GenAI Hackathon 2025 1st Runner-Up. I help businesses cut costs and automate workflows using GPT-4, LangChain, RAG, Next.js, FastAPI and Python. My 16 live systems have impacted ROI. I build chatbots, document AI, data pipelines and full-stack web apps. Industries served: Finance, Healthcare, Maritime, Energy and AgriTech. You get clean code, CI/CD, docs and a working product.... Saiba mais

Habilidades

s
simonsayed
Saidul
offline • 
Tempo médio de resposta: 1 hora

Conheça meus serviços

Redação de Roteiro
I will develop python scripts, automation bots, and web scrapers
A partir deUS$ 15 /projetoMais detalhes
Aplicações Web Full Stack
I will build a react next js web app with ai integration and backend API
A partir deUS$ 65 /projetoMais detalhes

Portfólio

Experiência profissional

Software Engineer

Jobswoop • Período integral

Nov 2025 - Mar 2026 • 4 mos

Contributed to a B2B recruitment and job-matching platform, collaborating with product teams to deliver data-driven improvements aligned with business goals. Analyzed complex data patterns using PostgreSQL to support business intelligence and platform optimization. Created technical documentation for APIs, system architecture, and database schemas to ensure clear communication. Participated in agile sprints, code reviews, stakeholder demos, and iterative delivery with continuous user feedback. Gathered requirements and delivered feature enhancements through cross-functional collaboration. Tech stack: Node.js, TypeScript, NestJS, PostgreSQL, AWS S3, REST APIs, Agile/Scrum.

Software Engineer

Rolog Solutions As • Período integral

Dec 2024 - Oct 2025 • 10 mos

Designed and deployed real-time monitoring and automated data analysis platforms for industrial warehouse robotics. Led the development of ARMADA (context-augmented anomaly detection, +43.5% precision) and FACTS (LLM-based document intelligence, 98% accuracy), achieving 97.1% reduction in model training time and 68–78% faster documentation search. Built robust data validation, anomaly detection, and alerting workflows, ensuring 99.5% uptime across 50+ robots. Delivered actionable insights, technical reports, and reproducible research for both technical and non-technical stakeholders using Python, FastAPI, pandas, PostgreSQL, OpenAI GPT, LangChain, and MLflow.