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)