Senior AI Architect - Computer Vision

GE Vernova•Greenville, NC

3d•$113,200 - $188,800

About The Position

The Senior AI Architect leads the Computer Vision Center of Excellence for Wind Engineering, establishing shared frameworks, standards, and reusable practices for reliable visual intelligence solutions across engineering initiatives. The role manages computer vision platforms, provisions project workspaces, access, and resources, and facilitates projects from onboarding through technical transfer. It guides imaging strategy, data acquisition and annotation, classical computer vision, deep learning, multimodal models, 2D/3D analysis, edge inference, and integration into engineering and field workflows. Through technical guidance, team mentorship, and consistent validation and governance practices, the role helps make vision solutions reliable engineering products with measurable inspection performance, traceable datasets, and sustainable deployment across manufacturing, laboratory, turbine, drone, robotic, and service contexts. The role leads development of reusable computer vision methods; establishes visual-data governance and model-development and validation standards; and guides 2D, 3D, multimodal, and spatial AI solutions. It also designs human-in-the-loop inspection workflows that communicate findings and evidence, provide review and escalation paths, and capture feedback while keeping engineering decisions accountable. GE Vernova describes a work environment with professional development, challenging careers, and competitive compensation. Relocation assistance is provided. For U.S.-based applicants, the stated pay range is $113,200 to $188,800, with geographic differentials in certain areas and discretionary annual bonus eligibility. The posting is expected to remain open for at least seven days after its stated posting date of October 21, 2026. Employment is conditional on successful completion of a drug screen where applicable, and candidates must be legally authorized to work in the United States.

Requirements

At least 8 years of experience.
Bachelor’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, Operations Research, or a related quantitative field.
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.

Nice To Haves

Advanced degree; strongly preferred.
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 other asset-intensive industries.
Experience with prognostics and health management, predictive maintenance, remaining useful life, anomaly detection, causal inference, optimization, or physics-informed machine learning.
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.

Responsibilities

Lead the Computer Vision Center of Excellence for Wind Engineering and establish shared frameworks for image acquisition, preprocessing, training, evaluation, deployment, monitoring, and feedback.
Establish reusable methods and approved patterns for detection, classification, segmentation, tracking, anomaly detection, optical character recognition, visual measurement, pose estimation, and 3D reconstruction.
Guide 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 designs for cameras, drones, robots, inspection devices, and engineering applications, promoting consistent practices and reuse.
Define standards for dataset curation, labeling taxonomies, annotation instructions, quality control, disagreement resolution, lineage, versioning, and access control.
Establish methods for addressing 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 recording intended use, coverage, limitations, provenance, and known failure modes.
Establish baseline methods, evaluation protocols, acceptance thresholds, and error-analysis practices for computer vision systems.
Require task-appropriate metrics, including precision, recall, F1, average precision, intersection-over-union, calibration, localization error, false-call rate, and missed-defect risk.
Define model validation across lighting, weather, backgrounds, camera configurations, product variants, sites, and operating conditions.
Ensure model comparisons account for engineering consequences and workflow economics, not only aggregate benchmark performance.
Guide architectures combining 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.
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 without obscuring required inspection evidence.
Provide technical guidance, conduct senior technical reviews, mentor practitioners, and communicate complex methods clearly.
Manage computer vision platforms, provision project workspaces, access, and resources, and facilitate projects from onboarding through technical transfer.

Benefits

Medical, dental, vision, and prescription drug coverage.
Access to Health Coach from GE Vernova, a 24/7 nurse-based resource.
Employee Assistance Program with 24/7 confidential assessment, counseling, and referral services.
GE Vernova Retirement Savings Plan, including a tax-advantaged 401(k) savings opportunity with company matching and company retirement contributions.
Access to Fidelity resources and financial planning consultants.
Tuition assistance.
Adoption assistance.
Paid parental leave.
Disability benefits.
Life insurance.
12 paid holidays.
Permissive time off.
Discretionary annual bonus eligibility.
Relocation assistance.
Professional development opportunities.