I build Python-based data, backend, and intelligent information systems.

My work focuses on the engineering path from raw information to reliable software capabilities:

Ingestion → Processing → Storage → Intelligence → Serving

I’m particularly interested in the systems behind intelligent applications. How information is structured, retrieved, evaluated, and exposed through dependable backend infrastructure.

My background is in Mechanical Engineering, which strongly influences how I approach software: I think in terms of systems, architecture, interfaces, constraints, data flow, failure modes, and measurable behavior.

My projects span multiple layers of a modern data and AI system:

• Data ingestion from APIs, files, and external sources
• ETL and data transformation pipelines
• Python analytics and machine-learning workflows
• Backend APIs and application services
• Automated data-processing workflows
• Database-backed systems
• Information retrieval and search pipelines
• Evaluation and observability of system behavior

I’m especially interested in the point where data engineering, backend engineering, and AI systems meet.

Completed

An end-to-end data intelligence platform built to automate the movement from external data sources to analytics, forecasting, insights, and application serving.

System flow:

External Sources → Ingestion → Processing → Intelligence → API → Application

Engineering areas:

• API integration
• Web scraping
• Automated ingestion
• ETL and data transformation
• Python analytics
• Forecasting and machine learning
• FastAPI
• Automated workflows
• Application serving
• Deployment and monitoring

ADIP established my foundation in building and integrating a complete data system across multiple technical layers.

Current

A backend-focused retrieval system designed to explore the infrastructure beneath intelligent information applications.

Core pipeline:

Document Ingestion → Chunking → Storage → Hybrid Retrieval → Reranking → Evaluation → Context Construction → Optional Generation

Engineering areas:

• Python backend development
• FastAPI
• PostgreSQL / SQL
• SQLAlchemy
• Alembic
• Redis
• Docker
• Lexical and semantic retrieval
• Embeddings and vector search
• Reranking
• Retrieval evaluation
• Background processing
• Testing
• Observability

A key design principle is separating retrieval quality, ranking quality, context construction, and answer generation so that system behavior can be measured and debugged independently.

RIE is the next step in my progression from broad data-system construction toward deeper backend, database, retrieval, and AI infrastructure engineering.

Python · SQL · JavaScript · HTML · CSS

FastAPI · REST APIs · Async Python · Modular application design

pandas · NumPy · ETL · Data transformation · API integration · Web scraping · Automation

PostgreSQL · SQLAlchemy · Alembic · Redis · Docker

scikit-learn · Prophet · Feature engineering · Forecasting · Model evaluation

Information Retrieval · Hybrid Search · Semantic Search · Embeddings · Vector Retrieval · Reranking · RAG Architecture · Retrieval Evaluation

Git · GitHub · GitHub Actions · Environment & dependency management · Deployment · Monitoring

Streamlit · Matplotlib · Plotly

Backend Engineering Python services, APIs, application structure, and system boundaries.

Data Engineering Ingestion, transformation, automation, and analytical data workflows.

Database Systems Relational data modeling, SQL, and database-backed applications.

Machine Learning & Analytics Forecasting, feature engineering, model evaluation, and analytical pipelines.

Information Retrieval & AI Systems Search, semantic retrieval, embeddings, reranking, evaluation, and RAG-oriented architectures.

Systems Engineering Architecture, integration, observability, testing, reliability, and maintainability.

I want to understand:

Architecture → Data Flow → Interfaces → Failure Modes → Evaluation → Trade-offs

That means treating traceability, evaluation, reliability, and maintainability as part of the engineering itself.

I prefer building systems where individual components can be understood, tested, measured, and improved independently.

My engineering trajectory is converging on:

I’m developing deeper capability in:

• Advanced Python
• Algorithms and data structures
• PostgreSQL and SQL
• Backend architecture
• Retrieval systems
• Embeddings and vector systems
• Ranking and evaluation
• Asynchronous processing
• Automated testing
• Observability
• Docker and CI/CD

The objective is to become increasingly capable of designing robust intelligent software systems from the underlying data and backend layers upward.

📧 Email: charleskohwo@gmail.com

💼 LinkedIn: linkedin.com/in/charles-onokohwomo