Senior AI Architect – Computer Vision

GE Vernova

. Lead the Computer Vision Center of Excellence for Wind Engineering .

Posted 10/21/2026full-timeSchenectady • New York • United StatesSenior💰 $113,200 - $188,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying machine-learning models, particularly in computer vision applications for engineering contexts. Proficient in statistical analysis, data management, and ensuring model performance aligns with operational decision-making.

Highest-signal resume keywords

Machine Learning Model DevelopmentComputer Vision TechniquesPython ProgrammingStatistical AnalysisWind Energy Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningData PreprocessingAnomaly DetectionOptical Character Recognition3D ReconstructionModel EvaluationDataset CurationFeature EngineeringExperimental DesignPredictive Maintenance

Soft Skills

Technical GuidanceMentorshipClear CommunicationCollaborationProblem Solving

Tools & Technologies

PandasNumPyScikit-LearnPyTorchTensorFlowXGBoostGPU WorkloadsModel RegistriesEnterprise Data PlatformsDistributed Training

Industry Keywords

Wind EngineeringAerospaceAutomotiveManufacturingPower GenerationReliability EngineeringAsset-Intensive IndustriesPrognostics and Health ManagementCausal InferencePhysics-Informed ML

Tech Stack

Tools & technologies

CloudNumpyPandasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Lead the Computer Vision Center of Excellence for Wind Engineering
Establish shared frameworks for acquisition, preprocessing, training, evaluation, deployment, monitoring, and feedback
Establish reusable methods 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
Align edge, cloud, and hybrid design choices for cameras, drones, robots, inspection devices, and engineering applications
Define dataset curation, labeling taxonomies, annotation instructions, quality control, disagreement resolution, lineage, versioning, and access-control standards
Establish methods for class imbalance, scarce defects, weak labels, synthetic data, augmentation, and active learning
Prevent data leakage across assets, sites, time periods, and near-duplicate imagery through appropriate dataset splits
Define dataset documentation covering intended use, coverage, limitations, provenance, and failure modes
Establish model baselines, evaluation protocols, acceptance thresholds, and error-analysis practices
Define task-appropriate metrics and validation across lighting, weather, backgrounds, camera configurations, product variants, sites, and operating conditions
Architect 2D, 3D, multimodal, and spatial AI solutions using RGB, thermal, depth, LiDAR, ultrasound, CAD geometry, inspection history, and engineering context
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
Manage geometric accuracy, coordinate systems, calibration, and uncertainty in measurement-oriented applications
Design human-in-the-loop inspection workflows presenting findings, confidence, evidence, and recommended actions
Establish review and escalation paths for uncertain, novel, safety-relevant, or high-consequence observations
Capture accepted, rejected, and corrected predictions to improve datasets and future model versions
Ensure AI augments accountable engineering decisions without obscuring required inspection evidence
Provide technical guidance, mentorship, validation, governance, and support for deployment across manufacturing, laboratory, turbine, drone, robotic, and service contexts

Requirements

What you’ll need

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 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
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
Legally authorized to work in the United States
Successful completion of a drug screen, as applicable

Benefits

Comp & perks

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