Edge AI / MLOps Engineer

About this role

About the role: - Engineer a computer-vision system that monitors production and dispatch operations for a large edible oil, soap, and soya meal manufacturer - Develop edge AI models that process video feeds from industrial cameras installed at exit gates and loading docks - Build inference pipelines that count items produced, match shipments against ERP orders, and trigger safety notifications when discrepancies occur - Deploy and optimize machine learning models to run on edge servers with minimal latency and resource constraints What you'll work on: - Training and fine-tuning vision models to handle challenging factory conditions such as worn or damaged packaging, similar product variants, wrapped pallets, high-speed conveyor lines, dim lighting, and unstable network connectivity - Designing the data pipeline from industrial cameras through edge processing to a central verification service - Building APIs and integrations between the edge AI layer, a web dashboard for operators, and mobile applications for dispatch teams - Monitoring model performance in production and retraining as needed when accuracy drifts due to environmental changes or new SKUs What we're looking for: - Strong experience with computer vision frameworks such as TensorFlow, PyTorch, or OpenCV - Proven background in MLOps: model versioning, testing, deployment pipelines, and monitoring - Hands-on knowledge of edge computing platforms and optimizing models for resource-constrained hardware - Ability to work with real-world, messy data and adapt solutions when field conditions differ from lab assumptions Note: Use the role name in the email subject. Send resume and links to relevant projects, GitHub, or published apps.