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kolade fajimi

@koladefajimi

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Backend engineer building secure, scalable Python-based distributed systems.

Nigeria

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What I'm looking for

I seek backend roles where I can build secure, scalable systems with Python/FastAPI, contribute to CI/CD and containerization, and grow in production operations, security, and machine-learning-informed backend features.

I am a backend engineer specializing in Python and FastAPI, focused on building secure, production-grade distributed systems that solve complex business logic. I emphasize clean code, automated testing, and scalable architectures including containerized deployments and CI/CD pipelines.

My projects include an AI document intelligence platform that offloads heavy workloads to Celery workers and uses JWT/RBAC for granular access control, and a production-ready e-commerce backend with Stripe integration, Redis caching, and optimized PostgreSQL schemas. I also developed a telco churn prediction model using Pandas and scikit-learn.

As an intern at the Nigerian Automotive Design and Development Council, I built Python diagnostic scripts for Raspberry Pi, automated hardware testing protocols, and contributed to system architecture and documentation. I hold a B.Eng. in Mechatronics Engineering and enjoy applying machine learning and IoT knowledge to backend systems.

Experience

Work history, roles, and key accomplishments

Backend Engineer

Nigerian Automotive Design and Development Council

Mar 2024 - Sep 2024 (6 months)

Developed Python-based diagnostic scripts and automated hardware testing protocols for vehicle automation sensors, reducing manual diagnostic time and contributing to system architecture design.

Python fastAPI PostgreSQL Redis Docker Celery JWT RBAC

Education

Degrees, certifications, and relevant coursework

Bells University of Technology

Bachelor of Engineering, Mechatronics Engineering

2020 -

Grade: 3.50 / 5.00

Pursuing a Bachelor of Engineering in Mechatronics Engineering with a final year project developing an IoT-enabled smart recycle bin using EfficientNet-Lite and real-time sensor pipelines.