Python / AI Engineering / Backend Architecture

I am a focused software engineer specializing in Python-based backend systems and AI engineering. I prioritize depth over noise and execution over hype. My goal is simple: build reliable systems, write clean code, and let the output speak for itself.

• Focus: Scalable Backend Architectures & Generative AI Integrations.
• Philosophy: Write code that scales. Design systems that endure. Secure everything.
• Location: Nairobi, Kenya 🇰🇪

Category	Tools & Technologies
Languages
Backend & Async
AI & Vector Ops
Data & MLOps
Infrastructure

• Advanced Python: OOP (Dunder methods), Decorators, Context Managers, and Type Hinting with Pydantic.
• Model Orchestration: Chaining prompts, managing RAG (Retrieval-Augmented Generation), and fine-tuning with Hugging Face.
• Data Engineering: Vectorized operations (NumPy/Pandas), DVC for data versioning, and ChromaDB/Pinecone for embeddings.
• Machine Learning: Scikit-Learn (Logistic Regression), Model Serialization (Pickle/Joblib), and Streamlit for AI Prototyping.

• Asynchronous Programming: Building high-concurrency systems using asyncio to handle non-blocking LLM API calls.
• Modern API Design: Production-grade REST/gRPC endpoints with FastAPI and Gunicorn.
• System Security: Secure coding practices, JWT/OAuth2 authentication, and web security protocols.
• Optimization: CPU/Memory profiling and efficient database management with SQL Workbench and PostgreSQL.

• Containerization: Architecting reproducible environments using Docker.
• Testing: Comprehensive unit testing with Pytest and "evals" for model output quality.
• Environment Management: Expert use of Venv, Conda, or Poetry for complex dependency isolation.

• Backend Engineering: Production-grade application design and RESTful API development.
• AI Implementation: LLM integrations and AI-assisted software systems.
• System Security: Secure coding practices and robust authentication systems.
• Efficiency: CI/CD pipelines and performance-optimized database management.