Computer Vision Engineer Healthcare AI Jobs UAE 2026

Ophthalytics — a fast-growing U.S. healthcare AI company supported by AWS, NVIDIA, and Microsoft, recognized as one of Georgia’s Top 10 Most Innovative Companies and recipient of the AWS Health Equity Award — is hiring a Computer Vision Engineer in the UAE with deep learning expertise and production ML experience on AWS. This mission-driven role focuses on building advanced medical imaging AI that can prevent blindness worldwide — using TensorFlow 2.x, PyTorch, YOLO, EfficientNet, ConvNeXt, and Vision Transformers to develop explainable AI models that detect vision-threatening diseases earlier, expanding access to eye care for millions of underserved communities globally. Compensation is paid in U.S. dollars (USD).

About Ophthalytics — Preventing Blindness Through Healthcare AI

Company: Ophthalytics — U.S. Healthcare AI Company | Mission: End Preventable Blindness Through AI

Technology: Advanced medical imaging AI · Explainable AI · Earlier detection of vision-threatening diseases

Impact: Expanding eye care access to rural and underserved communities worldwide through AI-powered screening

Recognitions: Georgia Top 10 Most Innovative 2025 · AWS Health Equity Award · AWS Portfolio Startup · NVIDIA Supported

Ecosystem: Supported by Microsoft, AWS, NVIDIA, and connected with Georgia Tech research ecosystems

Why This Ophthalytics Computer Vision Engineer Role in the UAE Is a Career-Defining Opportunity

Global Mission: Build AI that actively prevents blindness — working on technology with the potential to protect the vision of millions of people in underserved communities worldwide

USD Salary: Competitive compensation paid in U.S. dollars — exceptional financial value for UAE-based engineers

Technical Frontier: Work with Vision Transformers, EfficientNet, ConvNeXt, Explainable AI, and YOLO on medical imaging — the most technically advanced CV engineering available in healthcare

Leadership Access: Work directly with the Founder and top company leadership — extraordinary professional visibility and career acceleration

Position Overview

This Computer Vision Engineer — Healthcare AI role at Ophthalytics is a full-time, high-ownership, mission-driven engineering position for an exceptional computer vision and deep learning specialist with 3+ years of hands-on experience. You will build, train, evaluate, and deploy production-grade computer vision models for medical image analysis — including image classification, object detection, image segmentation, image quality assessment, and Explainable AI outputs — using TensorFlow 2.x, Keras, PyTorch, YOLO, EfficientNet, ConvNeXt, and Vision Transformers across the complete ML lifecycle on AWS infrastructure. You will design and maintain ML data pipelines, manage large medical image datasets, own model evaluation and benchmarking, convert research models to production using TensorFlow Serving, TensorRT, ONNX, and TensorFlow Lite, and contribute directly to a product that is actively expanding global access to vision-saving eye care. This is not a ticket-completion role — it is a high-ownership, fast-paced engineering career with direct influence on Ophthalytics’ product direction, technical priorities, and global health impact.

Why This Ophthalytics Computer Vision Engineer Role in the UAE Is the Healthcare AI Career of 2026: Computer Vision Engineers who combine expert-level TensorFlow and PyTorch proficiency with hands-on experience across the complete ML lifecycle — from data engineering through model training, evaluation, productionization, and AWS deployment — and who want their engineering work to genuinely matter beyond commercial metrics, are precisely the professionals Ophthalytics is building its world-class team around. The combination of a genuinely world-changing mission, USD compensation, direct founder access, NVIDIA and AWS backing, and the frontier technical challenges of medical image AI makes this one of the most professionally compelling and humanly meaningful Computer Vision Engineer roles available in the UAE in 2026.

Key Responsibilities

Computer Vision Model Development — Medical Imaging AI

Build, train, evaluate, and continuously improve computer vision models for ophthalmological medical image analysis — including image classification, multi-label classification, object detection, image segmentation, and image quality assessment and gating — directly supporting Ophthalytics’ mission to enable earlier detection of vision-threatening diseases across global patient populations
Implement and fine-tune modern computer vision architectures — including YOLO and other state-of-the-art object detection frameworks, EfficientNet and ConvNeXt for classification, and Vision Transformers including ViT and Swin — selecting and adapting the most appropriate architecture for each medical imaging task based on performance requirements, dataset characteristics, and deployment constraints
Build Explainable AI capabilities into Ophthalytics’ computer vision models — using techniques including Grad-CAM, saliency mapping, and attention visualization to produce clinically meaningful, transparent, and interpretable model outputs that support physician trust, clinical understanding, and real-world adoption of AI-assisted eye disease screening
Stay actively current with emerging computer vision architectures, multimodal AI approaches, and medical imaging AI research — evaluating new techniques for applicability to Ophthalytics’ specific ophthalmological imaging challenges and recommending adoption of advances that could meaningfully improve model performance, explainability, or clinical utility

Model Evaluation, Benchmarking & Clinical Performance Analysis

Own model evaluation, benchmarking, and performance reporting across Ophthalytics’ computer vision model portfolio — calculating and interpreting F1 score, AUROC, sensitivity, specificity, calibration, threshold optimization, and confusion matrix analysis with the statistical rigor and clinical domain awareness that medical AI evaluation requires
Conduct thorough false-positive and false-negative analysis and subgroup performance evaluation — ensuring that model performance is assessed not only at overall population level but across clinically meaningful patient subgroups, imaging conditions, and device types to identify and mitigate performance disparities that could affect the equity and reliability of AI-assisted screening deployment
Design and execute structured experiments to compare model architectures, training strategies, augmentation techniques, and optimization approaches — using systematic experiment tracking and model comparison frameworks to build a rigorous, reproducible evidence base for technical decision-making across Ophthalytics’ model development program

ML Data Engineering — Medical Image Dataset Management

Design and maintain reliable ML data pipelines for Ophthalytics’ medical imaging datasets — implementing robust data ingestion, cleaning, normalization, deduplication, and quality checking workflows that ensure all training, validation, and test data meets the quality standards required for responsible, clinically meaningful medical AI development
Manage dataset versioning, lineage, and metadata to maintain full reproducibility of training experiments — ensuring that every model trained at Ophthalytics can be precisely reconstructed from its data inputs, training configuration, and code version, supporting both scientific reproducibility and regulatory documentation requirements
Support image labeling and annotation workflow quality — collaborating with clinical annotators to ensure that ground truth labels for medical images are accurate, consistent, and well-documented, and implementing label quality verification checks that identify and resolve annotation errors before they affect model training
Build and maintain clear technical documentation for all large-scale medical image dataset processing, training pipeline configurations, and data management procedures — ensuring that the full data engineering foundation of Ophthalytics’ AI platform is documented, reproducible, and accessible to the engineering team

AWS ML Training, Production Deployment & Monitoring

Build and maintain scalable model-training workflows on AWS — using Amazon S3 for dataset and artifact storage, Amazon EC2 and GPU-based cloud infrastructure for model training, AWS IAM for access management, and Amazon CloudWatch for training job monitoring and alerting across Ophthalytics’ ML training infrastructure
Convert research-stage computer vision models into reliable, performant production systems — applying TensorFlow Serving, TensorRT, TF-TRT, TensorFlow Lite, TorchScript, and ONNX to optimize inference latency, GPU utilization, and throughput for deployment of Ophthalytics’ medical imaging AI across diverse hardware and deployment environment configurations
Implement production monitoring, performance tracking, and reliability workflows for deployed computer vision models — ensuring that model performance is actively monitored in production, that degradation or distribution shift is detected early, and that model versioning, release management, and rollback procedures support safe, reliable ongoing operation of Ophthalytics’ clinical AI platform
Apply preferred experience with AWS SageMaker, Amazon ECR, Amazon ECS/EKS, AWS Step Functions, and Terraform — leveraging MLOps, automated ML pipelines, and experiment tracking infrastructure to accelerate Ophthalytics’ model development and deployment cycles

Software Engineering Standards & Team Collaboration

Maintain high software engineering standards across all code contributions — writing clean, modular, well-documented, and testable Python code; participating actively in code reviews; managing work through Git and pull request workflows; and building reproducible development environments that enable reliable, consistent model development across the engineering team
Work closely with the Founder, top company leadership, and cross-functional teams — contributing to technical architecture decisions, product direction discussions, and execution priorities with the judgment, communication clarity, and collaborative spirit that a high-ownership, mission-driven startup engineering role demands

Qualifications & Requirements

Required Experience

3+ years of hands-on professional experience in computer vision or deep learning — with a strong portfolio demonstrating real-world model development, training, evaluation, and production deployment across classification, object detection, and segmentation tasks
Expert-level proficiency in TensorFlow 2.x and Keras — the primary development framework for this role — with strong working knowledge of PyTorch for cross-framework flexibility
Demonstrated experience with YOLO or equivalent detection frameworks; EfficientNet, ConvNeXt, or comparable classification architectures; and Vision Transformers in practical application contexts
Strong Python programming skills and software engineering practices — including clean code, modular design, testing, documentation, Git, and code review participation
Hands-on AWS experience including S3, EC2, IAM, and CloudWatch for ML training workloads, with GPU-based cloud infrastructure experience

How to Apply

Send your resume and relevant portfolio links to careers@ophthalytics.com with the email subject: CV Engineer
Include links to your GitHub repositories, computer vision projects, research publications, model development portfolio, production ML systems, and technical demonstrations where available

About Healthcare AI in the UAE 2026

Ophthalytics represents the most compelling intersection of technical excellence and genuine humanitarian purpose available in the UAE’s healthcare AI landscape in 2026. Millions of people globally — disproportionately those in rural and underserved communities without reliable access to ophthalmology specialists — are at risk of preventable blindness from conditions including diabetic retinopathy, glaucoma, and age-related macular degeneration that can be effectively managed when detected early. Ophthalytics’ AI-powered medical imaging technology creates the opportunity to dramatically expand access to earlier, more accurate detection — reaching patients who would otherwise never see an eye care specialist until it is too late to prevent vision loss. For Computer Vision Engineers in the UAE who want their technical skills to be deployed toward an outcome that genuinely matters — building AI that protects vision, expands access to healthcare, and serves the world’s most vulnerable communities — Ophthalytics’ Computer Vision Engineer role offers the most meaningful and professionally exceptional opportunity available in the UAE’s healthcare AI market in 2026.

Your Career Growth Path: Computer Vision Engineer → Senior CV Engineer → Principal AI Scientist → Head of Medical AI → Chief AI Officer — a globally recognized, mission-driven, and technically exceptional career trajectory at the intersection of computer vision engineering excellence and world-class healthcare AI impact.

Who Should Apply?

Computer Vision Engineers (3+ Years): With TensorFlow, PyTorch, YOLO, and AWS production deployment experience who want to apply their expertise to genuinely mission-critical medical imaging AI that prevents blindness globally
Medical Imaging AI Specialists: With healthcare AI, DICOM, or clinical workflow experience who want to work at a recognized, fast-growing U.S. healthcare AI company with USD compensation and direct founder access
Explainable AI Practitioners: With Grad-CAM, saliency mapping, or attention visualization experience who want to build the clinically transparent AI outputs that drive physician trust and real-world medical AI adoption
MLOps & Production ML Engineers (CV Focus): With TensorRT, ONNX, TensorFlow Serving, and AWS SageMaker experience who want to own end-to-end production ML deployment in a high-impact healthcare environment
Mission-Driven AI Engineers: Who want their engineering work to have tangible global health impact — directly contributing to the prevention of blindness in underserved communities through AI-powered eye disease screening