Fine-tune, deploy, and maintain multi-modal vision-language and transformer models for production. Build end-to-end training and inference pipelines in Python, implement prompt engineering and RAG, evaluate and iterate model performance, and optimize models (quantization) for efficient, scalable deployment across real-world environments.
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.
- 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
- 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
- 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)
The base pay range for this role is $100,000 – $120,000 per year.
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