Job Description Summary

The Senior AI Architect for Computer Vision leads the Computer Vision Center of Excellence for Wind Engineering, establishing shared frameworks, standards, and reusable practices that enable reliable visual intelligence solutions across engineering initiatives.

This 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 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 ensures vision solutions become reliable engineering products with measurable inspection performance, traceable datasets, and sustainable deployment across manufacturing, laboratory, turbine, drone, robotic and service contexts.

Job Description

Key Responsibilities

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.

Required Qualifications:

• 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

 

 

For candidates applying to a U.S. based position, the pay range for this position is between $113,200.00 and $188,800.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.

 

 

Bonus eligibility: discretionary annual bonus.

 

 

This posting is expected to remain open for at least seven days after it was posted on October 21, 2026.

 

 

Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.

 

 

GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.