I design and build intelligent, scalable AI systems that solve real-world problems using modern software architecture, cloud infrastructure, and cutting-edge machine learning.

Specializing in:

• Generative AI & LLM Applications
• Retrieval-Augmented Generation (RAG) Systems
• Multi-Agent AI Workflows & Orchestration
• Scalable Backend & Cloud-Native Architectures

I’m an AI & Software Engineer passionate about transforming ideas into intelligent, data-driven solutions.

My work sits at the intersection of AI research and software engineering, where I design and deploy production-ready systems powered by machine learning, cloud technologies, and modern backend architecture.

I enjoy:

• Architecting scalable AI platforms
• Designing data pipelines and vector retrieval systems
• Building multi-agent workflows
• Deploying real-time AI applications in the cloud

I’m continuously learning, building, and experimenting — from fine-tuning LLMs to architecting full-stack AI systems that make real impact.

• Python, PyTorch, TensorFlow
• Transformers, CNNs, NLP, Computer Vision
• LangChain, LangGraph, spaCy
• RAG Systems & LLM Application Design

• FastAPI, Flask, Spring Boot, Java
• REST APIs & Microservices Architecture
• Event-Driven Systems & Distributed Design

• Docker, Kubernetes, Terraform
• AWS, Google Cloud, Azure
• CI/CD Pipelines & Containerized Deployment

• PostgreSQL, MySQL, MongoDB, Neo4j
• Data Warehousing & Real-Time Streaming
• Vector Databases & Retrieval Pipelines

AI deal-sourcing pipeline that ingests YC startup data, enriches companies via web scraping, and scores them against a VC investment thesis using LLMs.

Key Highlights:

• Automated end-to-end VC analyst workflow: ingest → enrich → score → dashboard
• LLM scoring engine (Claude API) with structured JSON output validated by Pydantic
• Async Python backend (FastAPI + httpx) with rate-limited external calls
• React + TypeScript dashboard with filtering, sorting, and score breakdowns
• Dockerized multi-service architecture with CI/CD via GitHub Actions

🔗 https://github.com/HopeyCodeDS/venturesignal

End-to-end Retrieval-Augmented Generation system integrating vector search, LLM orchestration, and conversational memory.

Key Highlights:

• Reduced hallucinations using semantic retrieval pipelines
• Implemented LangChain agent workflows with tool calling
• Designed scalable FastAPI backend with vector database integration

🔗 https://github.com/HopeyCodeDS/RAG-based-chatbot-integration

AI system that automates extraction, validation, and structuring of logistics transport documents.

Impact:

• Reduced manual processing workload significantly
• Combined OCR, NLP, and validation pipelines
• Human-in-the-loop workflow for decision control

🔗 https://github.com/HopeyCodeDS/sortex-ai

Serverless API that answers natural-language queries against PDF documents using retrieval-augmented generation.

Impact:

• Grounded answers with source citations from uploaded PDFs
• Vector search (ChromaDB) + LLM (AWS Bedrock Claude) RAG pipeline
• Production-ready deployment on AWS Lambda via Docker

🔗 https://github.com/HopeyCodeDS/serverless-rag-api

OCR-powered application that automates warehouse label processing and matching workflows.

Features:

• Real-time label detection using computer vision
• End-to-end backend pipeline for document automation
• Mobile frontend integration for operational use

Backend: 🔗 https://github.com/HopeyCodeDS/automated-label-detection-and-matching-backend

Frontend: 🔗 https://github.com/HopeyCodeDS/automated-label-detection-and-matching-frontend

Containerized multi-service architecture deployed with Kubernetes for dynamic scalability.

Highlights:

• Microservices deployment using Docker & Kubernetes
• Auto-scaling and load balancing implementation
• Cloud-ready production architecture

🔗 https://github.com/HopeyCodeDS/multi-container-app

• NLP Sentiment Chat — Aspect-based sentiment analysis chatbot with local LLMs
• Mobility Application — Java transport management system
• CNN Emotion Recognition Model — Deep learning image classification system
• MineralFlow-KdG — Full-stack enterprise logistics platform with separate services: Frontend | Backend

👉 View all repositories: https://github.com/HopeyCodeDS?tab=repositories

LinkedIn:
https://www.linkedin.com/in/opeyemi-momodu-b92212b2/

Twitter / X:
https://twitter.com/opemomodu

I’m always open to collaborating on:

• AI system architecture projects
• LLM applications
• Multi-agent platforms
• Scalable cloud-based AI solutions

Feel free to connect or explore my work!