s
simonsayed

Saidul

@simonsayed

Full Stack AI Engineer LangChain RAG GPT4 NextJS FastAPI

Norwegen
Hindi, Bengalisch, Englisch
Einige Informationen werden in englischer Sprache angezeigt.
Über mich
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.... Mehr lesen

Kompetenzen

s
simonsayed
Saidul
offline • 
Durchschnittliche Antwortzeit: 1 Stunde

Meine Dienstleistungen

Scripting
I will develop python scripts, automation bots, and web scrapers
Ab15 $ / ProjektWeitere Details
Full-Stack-Webanwendungen
I will build a react next js web app with ai integration and backend API
Ab65 $ / ProjektWeitere Details

Portfolio

Arbeitserfahrung

Software Engineer

Jobswoop • Vollzeit

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 • Vollzeit

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.