Computer Vision

Overview

We are looking for a Computer Vision Engineer who can assess the technical feasibility of offline (on-device / edge) image recognition solutions, build and deploy YOLO-based detection models end-to-end, and also work with cloud-based computer vision services such as AWS Rekognition. Prior hands-on experience taking an offline model from research to production deployment is highly valued.

Key Responsibilities

• Conduct technical feasibility assessments for offline image recognition needs: architecture selection, dataset size, compute/hardware requirements (edge device, GPU/CPU), latency, and storage constraints before development begins.

• Design, train, and optimize image recognition / object detection models based on YOLO that can run fully offline (on-device/edge, no dependency on internet/cloud connectivity).

• Perform data preparation, annotation, augmentation, and model evaluation (precision, recall, mAP, etc.).

• Optimize models for deployment (quantization, pruning, conversion to lightweight formats such as ONNX / TensorRT / TFLite) so they run efficiently on target devices.

• Handle end-to-end deployment of computer vision models to production/edge devices, including post-deployment monitoring and maintenance.

• Explore and implement cloud-based computer vision / image recognition solutions (e.g., AWS Rekognition, Azure Computer Vision, Google Vision AI) as alternatives or complements to offline solutions.

• Provide trade-off recommendations between offline (custom model) and cloud-based (managed service) solutions based on business needs, cost, data privacy, and infrastructure conditions.

• Collaborate with product/engineering teams to integrate CV solutions into broader systems.

• Document research processes, experiments, and evaluation results in a structured way.

Qualifications

• Hands-on experience building computer vision / image recognition models using YOLO (or similar detection architectures).

• Proven, real experience building offline models — from research and training through to production/edge deployment (not just notebook-level experiments).

• Familiarity with cloud-based CV services such as AWS Rekognition (or equivalent), able to compare use cases against custom/offline solutions.

• Understanding of basic MLOps: model versioning, performance monitoring, retraining pipelines.

• Familiar with tools/frameworks: Python, PyTorch/TensorFlow, OpenCV, ONNX Runtime, Docker.

• Able to independently perform technical assessments (feasibility, effort, and risk) before project execution.

• Nice to have: experience with edge devices (Jetson, Raspberry Pi, etc.) or mobile deployment (TFLite/CoreML).

Requirements

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.

• Minimum 2-5 years of hands-on experience in computer vision / machine learning.

• Demonstrated portfolio or GitHub of past computer vision / object detection projects, ideally including at least one model taken to production or edge deployment.

• Solid understanding of mathematics relevant to computer vision (linear algebra, probability, optimization).

• Strong problem-solving and analytical skills, with the ability to independently judge project feasibility, effort, and risk.

• Good communication skills, able to explain technical trade-offs (offline vs. cloud-based) to non-technical stakeholders.

• Comfortable working cross-functionally with product and engineering teams.

• Willing to work on-site/hybrid as needed for hardware testing and edge-device deployment (adjust based on company policy).