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)