Computer Vision Engineer

About the Role

We are looking for a Computer Vision Engineer to help design, build, and optimize computer vision systems that power AI-driven operational intelligence platforms. This role will work closely with Engineering, Product, Infrastructure, and Analytics teams to develop scalable computer vision pipelines and AI-driven systems that process real-world operational data from distributed edge environments. The ideal candidate is a strong engineer with experience building production-grade computer vision solutions and solving complex technical challenges involving video streams, real-time inference, edge deployments, and AI-powered operational workflows. This position requires strong systems thinking, practical engineering skills, and the ability to collaborate across backend systems, edge infrastructure, analytics platforms, and operational products.

Key Responsibilities

Design, build, and optimize computer vision pipelines for operational analytics systems.

Develop scalable processing workflows for video streams and image-based data.

Improve inference pipelines, detection accuracy, and model performance.

Build reliable systems for real-time and near real-time computer vision processing.

Develop and integrate computer vision models into production environments.

Work with object detection, tracking, classification, segmentation, and spatial analysis workflows.

Optimize models for edge deployments, inference speed, resource utilization, and operational scalability.

Support integrations between computer vision pipelines, backend services, analytics platforms, and distributed edge-server infrastructure.

Process operational and behavioral data generated from computer vision systems.

Troubleshoot production issues related to computer vision workflows and distributed systems.

Collaborate with Backend Engineers, DevOps, Product Managers, and Analytics teams.

Participate in architecture discussions, technical planning, documentation, and engineering best practices.

Must-have Requirements

4+ years of experience in Computer Vision Engineering or Machine Learning Engineering.

Strong experience building production-grade computer vision systems.

Strong Python programming skills.

Experience with OpenCV and modern computer vision frameworks.

Experience working with deep learning frameworks such as PyTorch or TensorFlow.

Experience working with video processing, image analysis, or real-time inference pipelines.

Experience deploying AI/ML systems into scalable production environments.

Understanding of object detection, tracking, segmentation, and image classification workflows.

Experience optimizing inference pipelines and model performance.

Familiarity with GPU acceleration and model optimization techniques.

Experience working with distributed systems or edge deployments.

Understanding of distributed systems and edge-server architectures.

Experience integrating computer vision systems with backend APIs and operational platforms.

Familiarity with real-time processing systems and event-driven architectures.

Understanding of monitoring, observability, and production reliability practices.

Strong debugging and analytical problem-solving skills.

Experience collaborating in Agile/Scrum engineering environments.

Strong communication skills in cross-functional engineering environments.

Ability to work autonomously in remote environments.

Ownership mentality and proactive problem-solving approach.

Fluent English.

Nice-to-have Requirements

Experience with spatial intelligence or operational analytics platforms.

Experience working with IoT or edge devices.

Familiarity with Node.js or TypeScript backend systems.

Experience with cloud infrastructure and GPU-based deployments.

Experience with MLOps or model lifecycle management.

Experience with AWS.

Experience with Docker.

Experience with CI/CD pipelines.

Experience with monitoring and observability tooling.

Experience with ONNX or TensorRT.

Experience with Kafka or event-streaming systems.

Experience with Kubernetes.

Familiarity with edge inference frameworks.

Experience with real-time video processing systems.

Familiarity with PostgreSQL.