COMPUTER VISION ENGINEER (LLM & AI Integration)

Duncan & Ross; Abu Dhabi; On-site; AED 180,000 - 280,000; Full time

Job summary

Duncan & Ross in Abu Dhabi seeks a Computer Vision Engineer with AI integration expertise to design, build, and deploy multimodal vision systems that work with LLMs.

The role focuses on bridging image understanding and natural language processing, developing pipelines across text, images, and video, and deploying production-ready models with PyTorch, TensorFlow, and OpenCV. Strong cloud experience required.

Qualifications

• Degree in computer science, AI, CV, or related field; PhD preferred.
• 3–7 years in computer vision or multimodal AI.
• Strong Python and DL framework experience.

Responsibilities

• Develop CV models for image classification, detection, segmentation, and recognition.
• Integrate CV models with LLMs to describe visual content.
• Design multimodal pipelines combining text, images, and video.
• Prototype and deploy models using TensorFlow, PyTorch, and OpenCV.
• Collaborate with data scientists to fine-tune vision-language models.
• Ensure ethical AI practices and data privacy in deployments.

Skills

Python

PyTorch

TensorFlow

Keras

OpenCV

LLM integration

multimodal AI

cloud platforms

NVIDIA Jetson / Edge deployment

Education

Bachelor's or Master's degree in CS/AI/CV

PhD preferred

Tools

Azure AI

AWS SageMaker

Google Vertex AI

OpenVINO

ONNX

NVIDIA Jetson

Job description

About the job COMPUTER VISION ENGINEER (LLM & AI Integration)

JOB SUMMARY:

We are seeking an experienced Computer Vision Engineer with a strong background in AI and Large Language Models (LLMs). The ideal candidate will design, build, and deploy computer vision solutions that integrate with generative AI and LLM frameworks to interpret, analyze, and describe visual data. This role bridges the gap between image understanding and natural language processing, enabling intelligent visual-language applications.

KEY RESPONSIBILITIES:

• Develop and implement computer vision models for image classification, object detection, segmentation, facial recognition, and visual understanding.
• Integrate vision models with LLMs (e.g., GPT, LLaVA, CLIP, or multimodal models) to build systems that interpret and describe visual content.
• Design AI pipelines that combine text, images, and video data for multimodal learning and reasoning.
• Utilize deep learning frameworks (TensorFlow, PyTorch, OpenCV) to prototype and deploy models.
• Collaborate with data scientists and AI researchers to fine-tune vision-language models for specific tasks such as visual QA, captioning, or scene analysis.
• Implement data preprocessing, augmentation, and annotation pipelines for large-scale image datasets.
• Conduct performance benchmarking, optimization, and deployment of models in production environments.
• Research and experiment with emerging techniques in Generative AI, multimodal transformers, and neural architecture optimization.
• Develop APIs and tools for internal teams to utilize vision + LLM capabilities.
• Ensure compliance with ethical AI practices, including bias mitigation and data privacy.

QUALIFICATIONS:

• Bachelors or Masters degree in Computer Science, AI, Computer Vision, or related field (PhD preferred).
• 3-7 years of experience in computer vision, deep learning, or multimodal AI.
• Strong proficiency in Python and frameworks such as PyTorch, TensorFlow, Keras, and OpenCV.
• Experience integrating LLMs (GPT, Claude, Gemini, or open-source models) with vision systems.
• Solid understanding of transformer architectures, CNNs, diffusion models, and attention mechanisms.
• Familiarity with multimodal datasets (COCO, Visual Genome, etc.) and evaluation metrics for vision tasks.
• Experience with cloud-based AI tools (Azure AI, AWS Sagemaker, Google Vertex AI, etc.).
• Ability to write clean, scalable, and production-grade code.
• Strong analytical, problem-solving, and communication skills.

PREFERRED QUALIFICATIONS:

• Experience with multimodal LLM frameworks such as CLIP, BLIP, LLaVA, or Kosmos-2.
• Background in natural language processing and prompt engineering.
• Hands-on experience with edge deployment (NVIDIA Jetson, OpenVINO, ONNX).
• Knowledge of reinforcement learning, generative models, or 3D vision.
• Publications or open-source contributions in AI research are a plus.