Vision AI Engineer

ClearObject | United States (Hybrid)

Join an extraordinary team in a rapidly evolving industry!

ClearObject has been a pioneer in driving digital innovation for over a decade. We specialize in leveraging cutting-edge technologies such as edge-based artificial intelligence (AI), generative AI, Computer Vision, and Cloud solutions to transform raw data into actionable intelligence. Our solutions empower businesses across diverse industries to optimize operations, elevate customer experiences, and achieve sustainable growth.

As a proud Google Premier Partner, Google GenAI Launch Partner, AWS Select Partner, and IBM Gold Business Partner, we are committed to driving environmental sustainability through our projects, ensuring that every contribution makes a positive impact.

Job Summary

We are seeking a highly motivated and experienced Vision AI Engineer to join our growing team focused on building and deploying advanced Vision AI solutions. In this role, you will partner with our lead engineers to specify, build, deploy and operate the full lifecycle of Vision AI initiatives. You will serve as the technical authority on Vision AI/ML, collaborating closely with data scientists, ML engineers, and customers to bring intelligent vision systems to production.

This is an ideal role for a technically deep engineer with hands-on experience in computer vision, edge AI, and systems programming who thrives in a fast-paced, innovation-driven environment.

Responsibilities

Vision AI Solution Scoping & Design

Support technical scoping engagements with customers to define Vision AI requirements, use cases, and success criteria.
Support the design of end-to-end Vision AI system architectures, including camera infrastructure, edge compute, networking, and cloud integration.
Create detailed documentation including system diagrams, network topologies, data flow diagrams, and deployment procedures.
Collaborate with data scientists and ML engineers to define model requirements, dataset strategies, and inference pipeline design.

Hardware Configuration & Integration

Research, select, and procure hardware components — cameras, lenses, lighting, GPUs, edge compute units — matched to Vision AI workload requirements.
Configure, calibrate, and validate hardware for optimal performance within Vision AI pipelines.
Manage on-site hardware installations, upgrades, and troubleshooting.
Apply knowledge of lighting techniques, optics, and image quality optimization to ensure reliable vision system inputs.

Linux System Configuration & Management

Configure and administer Linux-based systems supporting Vision AI workloads, including OS tuning, driver management, and security hardening.
Develop and maintain shell scripts and automation tooling for system provisioning, monitoring, and maintenance.
Manage containerized deployments using Docker and related technologies.
Configure network connectivity, protocols (TCP/IP), and security for vision systems, ensuring reliable and secure data transfer.

Cloud & Edge AI Deployment

Deploy and manage Vision AI workloads across cloud platforms (GCP, AWS) and edge environments.
Integrate vision systems with cloud-based AI/ML platforms, data pipelines, storage, and APIs.
Optimize inference performance across edge and cloud targets, balancing latency, throughput, and cost.
Implement CI/CD practices and infrastructure-as-code approaches for repeatable, scalable deployments.
Monitor production Vision AI workloads, respond to operational incidents across distributed edge fleets.

Software Development

Develop software components for Vision AI systems, including inference engines, data preprocessing pipelines, and system integration layers.
Write production-quality code in Python and/or C/C++ for AI integration and systems-level applications.
Integrate Vision AI models with existing production systems and customer environments.
Troubleshoot and resolve software, networking, and system-level issues across the full stack.

Required Qualifications

Education: Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field.
Experience: 1+ years deploying, integrating, or operating computer vision or ML systems in production environments. Field deployment, edge integration, or production support experience strongly preferred.
Strong working knowledge of Vision AI/ML principles, with hands-on experience deploying, integrating, and operating models in production. Model training expertise is not required for this role.
Proficiency in Linux system administration, shell scripting, and command-line tooling.
Hands-on experience with hardware selection, configuration, and integration for vision systems (cameras, lenses, lighting, edge compute).
Experience with network configuration, TCP/IP protocols, and security practices for interconnected systems.
Software development proficiency in Python; experience with C/C++ or other systems-level languages a strong plus.
Hands-on experience with at least one computer vision framework (OpenCV, PyTorch, TensorFlow, or ONNX runtime) at a deployment and integration level.
Working knowledge of Docker and containerized deployment patterns.
Practical understanding of image quality fundamentals — exposure, focus, lighting, and lens selection — for production vision systems.

Preferred Skills & Experience

Vision AI & Machine Learning

Experience with computer vision frameworks and libraries: OpenCV, TensorFlow, PyTorch, ONNX, TensorRT, or similar.
Familiarity with image processing, object detection, classification, segmentation, and anomaly detection techniques.
Experience with Vision AI model optimization for edge inference (quantization, pruning, model conversion).
Exposure to generative AI or multi-modal vision-language models is a plus.
Experience with sensor fusion (e.g., combining camera, LiDAR, radar, or other sensor inputs) or multi-modal AI systems that integrate vision with additional data modalities is a plus.

Cloud & Infrastructure

Hands-on experience with GCP (Vertex AI, Cloud Vision, GKE) and/or AWS (SageMaker, Rekognition, ECS) AI/ML services.
Experience with Kubernetes/GKE for containerized ML deployment patterns at scale.
Experience with infrastructure-as-code tools (Terraform, Ansible) and CI/CD pipelines for ML systems.

Software Development

Experience building production AI pipelines, REST APIs, or data integration services.
Familiarity with industrial automation systems, PLCs, or OT/IT integration is a plus.
Experience with MLOps practices and tooling.

Competencies

Problem-Solving: Ability to diagnose complex system issues across hardware, software, and network layers and drive effective resolution.
Technical Depth: Capacity to engage at a systems level — from camera optics and edge hardware to cloud APIs and ML model behavior.
Communication: Strong written and verbal skills; able to communicate clearly with customer technical staff during deployment and troubleshooting, and to translate technical concepts for engineering teammates.
Collaboration: Effective at working across cross-functional teams including data scientists, ML engineers, and customer technical leads.
Adaptability: Comfortable navigating shifting requirements and emerging technologies in a fast-moving AI landscape.
Continuous Learning: Genuine commitment to staying current with advancements in Vision AI, edge compute, and cloud AI platforms.

Additional Information

To qualify, applicants must be legally authorized to work in the United States and should not require, now or in the future, sponsorship for employment visa status.

Ability to lift up to 40 pounds and work on your feet for short durations.

This position may require travel up to 20% of the time. International travel is possible but not required.

ClearObject is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.

The level of this position will be determined based on the applicant's education, skills, and experience.

ClearObject employees performing certain job functions may require access to technology or software subject to export or import regulations. To comply with these regulations, ClearObject may obtain nationality or citizenship information from applicants for employment. ClearObject collects this information solely for trade law compliance purposes and does not use it to discriminate unfairly in the hiring process.

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