OT Artificial Intelligence Computer Vision Engineer (ACE AI Project)

OT Artificial Intelligence Computer Vision Engineer

OT Artificial Intelligence | OT Product Line

Position Overview

We are seeking an OT Artificial Intelligence Computer Vision Engineer to design, develop, deploy and operate production-grade vision solutions for industrial environments. This role turns images, video and operational context into dependable capabilities for inspection, safety monitoring, asset condition assessment, anomaly detection, remote operations, robotics and autonomous systems.

This position is part of an industrial AI team and provides hands-on computer vision engineering leadership and delivery support for operational technology (OT) AI use cases. The role builds reusable approaches for visual data acquisition, annotation, model development, validation, optimization, deployment and monitoring across fixed cameras, mobile devices, drones, robots, edge systems and approved cloud environments.

The team is highly technical, creative and cross-functional. The Computer Vision Engineer will work with industrial domain experts, data engineers, machine learning engineers, solution architects, cybersecurity teams and operations personnel to move vision solutions from controlled experimentation into sustained field use.

Success means delivering measurable outcomes while ensuring performance remains dependable across changing lighting, weather, viewpoints, backgrounds, equipment states, connectivity and compute constraints.

High-Level Job Description / Scope

This is a dedicated computer vision engineering role supporting industrial AI initiatives. It covers visual sensing strategy, camera and imaging requirements, dataset design, annotation, model training, evaluation, optimization, deployment, monitoring, retraining and retirement.

The role supports real-time and batch analysis of visible, thermal and other approved imagery while maintaining privacy, cybersecurity, human oversight and safe fallback behavior.

Key Responsibilities

1. Translate industrial inspection, safety, reliability and automation problems into computer vision objectives, measurable success criteria and technically viable solution designs.

2. Develop, train and evaluate models for object detection, classification, segmentation, tracking, pose estimation, optical character recognition, change detection and visual anomaly detection.

3. Define camera, lens, illumination, field-of-view, resolution, frame-rate, placement and environmental requirements with OT and engineering stakeholders.

4. Design representative image and video datasets, including collection plans, annotation standards, ground truth, sampling, balancing, augmentation and synthetic-data needs.

5. Establish reproducible experimentation, model versioning, evaluation and approval workflows using disciplined MLOps practices.

6. Package and deploy computer vision models for secure inference on industrial edge devices, cameras, gateways, robots, drones, on-premises platforms and approved cloud services.

7. Optimize vision models and pipelines for accuracy, latency, throughput, memory, compute, energy consumption and bandwidth constraints.

8. Validate performance across representative lighting, weather, motion, occlusion, viewpoints, backgrounds, equipment states, rare events and degraded operating conditions.

9. Implement monitoring for detection quality, confidence, false alarms, missed events, visual drift, camera health, data quality, resource utilization and operational impact.

10. Define retraining triggers, rollback procedures, fallback behavior and model retirement criteria with architecture, cybersecurity and operations teams.

11. Integrate vision outputs with industrial workflows, alerting, historians, asset context, inspection systems, robotics platforms and human review processes.

12. Produce model cards, dataset documentation, test evidence, camera configuration records, deployment runbooks and support procedures.

13. Contribute actively within an Agile delivery team and continuously improve shared computer vision standards, components and engineering practices.

Required Qualifications

1. Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, Artificial Intelligence or a related field, or equivalent experience.

2. Five or more years of experience developing and deploying production computer vision or machine learning solutions.

3. Strong programming skills in Python and experience with production software engineering, testing, source control, code review and CI/CD.

4. Hands-on experience with computer vision and deep learning frameworks such as OpenCV, PyTorch, TensorFlow or equivalent technologies.

5. Experience with object detection, classification, segmentation, tracking, image preprocessing, augmentation, evaluation and error analysis.

6. Experience preparing and governing image or video datasets, including collection, annotation, quality review, ground truth, lineage and versioning.

7. Experience deploying and monitoring vision models using containers, APIs, model registries, orchestration and automated delivery pipelines.

8. Experience evaluating camera placement, field of view, resolution, frame rate, lighting and environmental factors that affect model performance.

9. Understanding of OT cybersecurity, network segmentation, least-privilege access and the constraints of industrial environments.

10. Ability to communicate vision-system behavior, uncertainty, limitations and operational risk to engineers, operators, architects, cybersecurity reviewers and business stakeholders.

11. Demonstrated ownership of production vision services, including troubleshooting, incident response, documentation and continuous improvement.

Preferred Qualifications

1. Master's degree in Computer Science, Computer Vision, Robotics, Artificial Intelligence or a related field.

2. Experience with Microsoft Azure AI services, Azure Machine Learning, Azure AI Vision or equivalent platforms.

3. Experience delivering industrial inspection, leak detection, corrosion, flame or flare monitoring, PPE, perimeter, equipment-state or safety-related vision use cases.

4. Experience with thermal, infrared, multispectral, depth, stereo or other specialized imaging modalities.

5. Experience integrating cameras and vision workloads with drones, robots, fixed inspection systems or autonomous platforms.

6. Experience deploying optimized edge inference using GPUs, NPUs or other AI accelerators in constrained or intermittently connected settings.

7. Experience with model compression, quantization, pruning, distillation, ONNX, TensorRT or hardware-aware optimization.

8. Experience with video analytics, multi-camera tracking, three-dimensional vision, visual localization, photogrammetry or sensor fusion.

9. Experience with responsible AI, privacy-preserving vision, explainability, adversarial testing or AI assurance.

10. Experience in industrial environments, including energy, manufacturing or other asset-intensive industries, and comfort working directly with asset and operations personnel.

Critical Selection Criteria

1. Computer Vision Capability

Demonstrated ability to design, train, validate, deploy and sustain production vision models and end-to-end imaging solutions using disciplined software engineering and MLOps practices.

2. Field Performance

Ability to account for cameras, lighting, weather, motion, occlusion, viewpoint, background and operational variability, and to implement effective monitoring, human review, fallback and rollback mechanisms.

3. OT and AI Understanding

Knowledge of industrial operations, visual inspection workflows, OT cybersecurity and the lifecycle required to move vision solutions from experimentation into dependable field use.

4. Collaboration and Communication

Ability to translate operational problems into measurable computer vision objectives and clearly explain performance, uncertainty, false-alarm trade-offs, limitations and risks to technical and nontechnical stakeholders.