Computer Vision AI Engineer

We're seeking a Computer Vision AI Engineer with deep experience in transformers, generative models, and vision-language models (VLMs) to push City Detect's products beyond traditional object detection. You'll fine-tune, deploy, and maintain multi-modal models that combine visual and language understanding to deliver intelligent, scalable solutions across heterogeneous real-world environments. What You'll Do Fine-tune and deploy vision-language models (VLMs) and large language models for production use cases Design and maintain end-to-end pipelines for multi-modal model training, evaluation, and inference in Python Develop prompt engineering strategies, RAG architectures, and other techniques to maximize model performance Evaluate model outputs systematically and build feedback loops for continuous improvement Quantize large transformer models to improve model efficiency Stay current with rapid advances in transformer architectures, fine-tuning methods, and multi-modal research Requirements 3+ years of professional experience working with transformer-based architectures 2+ years of hands-on experience fine-tuning and deploying multi-modal models (VLMs) 2+ years of proven computer vision experience, with a strong preference for object detection Strong experience with LLMs — fine-tuning, inference optimization, and production deployment Proficiency in Python for model development, training, and deployment (2+ years) Experience with deep learning frameworks such as PyTorch or TensorFlow Solid understanding of attention mechanisms, tokenization, transfer learning, and generative model fundamentals Proven experience taking models from experimentation through production-ready deployment Nice to Have SQL proficiency for querying detection results, labeling metrics, or model performance data Strong preference : experience with roadside or infrastructure object detection (signs, signals, debris, pavement markings) Background in GovTech, public sector, or smart city projects Experience in automated driving, ADAS, or autonomous vehicle perception systems Familiarity with model-assisted labeling, active learning, or human-in-the-loop workflows Experience with edge deployment or model optimization (TensorRT, ONNX, quantization) Compensation The base pay range for this role is $100,000 – $120,000 per year.