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)

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Computer Vision AI Engineer