that operate reliably in the real world.
Turning complex requirements into production-grade software — ML integrations, full-stack systems, and data-driven architectures built to last.
Engineering challenges solved end-to-end — from architectural decisions through to production delivery.
An AI-powered crop yield estimation feature integrated into FarmaNet's agri-tech platform — built the complete ML pipeline and multi-service architecture ready for production model ingestion.
Full-stack developer on a production web application for a yoghurt business — owning the complete delivery lifecycle from requirements engineering through to deployment.
During national service at ECG, a major fibre cut disrupted network connectivity and critical services across a branch. Led coordinated infrastructure recovery and restored normal operations.
Translating operational challenges into structured, production-grade software systems.
End-to-end ML service integration — predictive models, classification pipelines, and automated decision workflows with well-defined API boundaries.
Purpose-engineered applications for managing operations, data, and workflows — dashboards, internal tooling, and structured business portals.
When the problem outgrows off-the-shelf tooling — requirements clarification, coherent system architecture, and tailored software delivery.
Engineering philosophy, professional background, and what drives the work.
I am a Computer Science graduate specialising in software engineering, AI, and ML engineering. My foundation spans software architecture, computational problem-solving, and systems thinking — applied across backend development, system integration, distributed systems, and intelligent system design.
During my national service at the Electricity Company of Ghana (ECG), I worked within enterprise-grade IT infrastructure — managing server configurations, maintaining network systems, and leading recovery operations.
Currently at FarmaNet, I am part of the AI engineering team developing a growing suite of machine learning technologies that drive intelligent decision-making across agricultural operations. My core work involves designing and deploying predictive models — crop estimation systems among them — that translate raw operational data into actionable intelligence. Beyond any single project or company, what I am building toward is software and AI systems that close the gap between technical capability and real-world impact — wherever that gap exists.
From early-stage technical scoping to full system delivery — structured, practical solutions that are reliable to build and straightforward to maintain.
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