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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Architect - Computer Vision - **Company:** GE Vernova - **Location:** Greenville, SC, United States - **Experience:** Expert - **Salary:** $113,200.0 - $188,800.0 - **Contract:** Permanent contract - **Skills:** Computer-Aided Design, Artificial Intelligence, Optical Character Recognition (OCR), Computer Vision, Data Files, Information Leak Prevention, Data Transformation, Distributed Data Store, Python (Programming Language), Machine Learning, Language Modeling, NumPy, Reliability Engineering, Tensorflow, Management of Software Versions, Enterprise Data Management, Feature Store, Pytorch, Uncertainty Quantification (UQ), Deep Learning, Model Validation, Pandas, Scikit Learn, Information Technology, Xgboost, Lidar, Unsupervised Learning - **Published:** October 23, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f0e8340b231caa67 ## About the Role * Minimum 8 years experience along with Bachelor's degree in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, Operations Research, or a related quantitative field; advanced degree strongly preferred. * Significant hands-on experience developing, validating, deploying, and monitoring machine-learning or deep-learning models in complex real-world environments. * Strong foundation in statistics, experimental design, supervised and unsupervised learning, model selection, generalization, calibration, and uncertainty. * Experience with Python and common data science frameworks such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent. * Experience with time-series or industrial data, including temporal validation, sensor quality, operating regimes, sparse events, and non-stationarity. Preferred Candidate Characteristics * Demonstrated ability to detect data leakage, bias, confounding, overfitting, and invalid evaluation designs. * Experience translating model performance into engineering acceptance criteria and operational decision value. * Ability to define enterprise practices, conduct senior technical reviews, mentor practitioners, and communicate complex methods clearly. * Experience in wind energy, aerospace, automotive, manufacturing, power generation, reliability engineering, or asset-intensive industries. * Experience with prognostics and health management, predictive maintenance, remaining useful life, anomaly detection, causal inference, optimization, or physics-informed ML. * Experience with distributed training, GPU workloads, feature stores, experiment tracking, model registries, and enterprise data platforms. * Familiarity with survival analysis, Bayesian methods, probabilistic modeling, uncertainty quantification, or decision analysis. * Experience with explainability and model-risk documentation for engineering or regulated decisions. * Experience leading technical standards and communities of practice across distributed data science teams ## Description 1. Lead the Computer Vision Center of Excellence for Wind Engineering * Lead the Computer Vision Center of Excellence, establishing shared frameworks for acquisition, preprocessing, training, evaluation, deployment, monitoring, and feedback across Wind Engineering initiatives. * Establish reusable methods and approved patterns for detection, classification, segmentation, tracking, anomaly detection, optical character recognition, visual measurement, pose estimation, and 3D reconstruction. * Guide consistent selection of classical computer vision, deep learning, vision-language models, and hybrid approaches based on accuracy, explainability, latency, data availability, and operational risk. * Align teams on edge, cloud, and hybrid design choices for cameras, drones, robots, inspection devices, and engineering applications, promoting consistent practices and reuse across initiatives. 2. Create Visual Data, Annotation, and Dataset Governance Practices * Define standards for dataset curation, labeling taxonomies, annotation instructions, quality control, disagreement resolution, lineage, versioning, and access control. * Establish methods for handling class imbalance, scarce defects, weak labels, synthetic data, augmentation, and active learning. * Require training, validation, and test splits that prevent leakage across assets, sites, time periods, and near-duplicate imagery. * Define dataset documentation that records intended use, coverage, limitations, provenance, and known failure modes. 3. Define Model Development and Validation Standards * Establish baseline methods, evaluation protocols, acceptance thresholds, and error-analysis practices for computer vision systems. * Require metrics suited to the task, including precision, recall, F1, average precision, intersection-over-union, calibration, localization error, false-call rate, and missed-defect risk. * Define validation across lighting, weather, backgrounds, camera configurations, product variants, sites, and operational conditions. * Ensure model comparisons consider engineering consequences and workflow economics, not only aggregate benchmark performance. 4. Architect 2D, 3D, Multimodal, and Spatial AI Solutions * Guide architectures that combine RGB, thermal, depth, LiDAR, ultrasound imagery, CAD geometry, inspection history, and engineering context where appropriate. * Define patterns for photogrammetry, point-cloud processing, registration, dimensional analysis, change detection, and digital-twin alignment. * Evaluate vision-language and multimodal foundation models for search, triage, assisted inspection, and reasoning while preserving task-specific validation. * Ensure geometric accuracy, coordinate systems, calibration, and uncertainty are explicitly managed in measurement-oriented applications. 5. Design Human-in-the-Loop Inspection Workflows * Define how models present findings, confidence, evidence, and recommended actions to inspectors and engineers. * Establish review and escalation paths for uncertain, novel, safety-relevant, or high-consequence observations. * Design feedback capture so accepted, rejected, and corrected predictions improve datasets and future model versions. * Ensure AI augments accountable engineering decisions and does not obscure required inspection evidence.