Senior AI Architect – Computer Vision

GE Vernova

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

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading computer vision initiatives, including model development, evaluation, and deployment in complex environments. Proficient in statistical analysis, machine learning frameworks, and industry-specific applications within wind energy and asset-intensive sectors.

Highest-signal resume keywords

Computer Vision LeadershipMachine Learning Model DevelopmentStatistical AnalysisPython ProgrammingWind Energy Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningStatistical AnalysisModel EvaluationData Annotation2D/3D AnalysisImage ProcessingData Leakage DetectionPredictive MaintenanceUncertainty Quantification

Soft Skills

Technical GuidanceMentoringClear CommunicationProject ManagementCollaboration

Tools & Technologies

PythonPandasNumPyScikit-LearnPyTorchTensorFlowXGBoostGPU WorkloadsFeature StoresModel Registries

Industry Keywords

Wind EnergyAerospaceAutomotiveManufacturingPower GenerationReliability EngineeringAsset-Intensive IndustriesPrognostics and Health ManagementAnomaly DetectionPhysics-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, standards, reusable methods, and approved patterns across computer vision initiatives
Guide acquisition, preprocessing, training, evaluation, deployment, monitoring, and feedback practices
Manage computer vision platforms and provision project workspaces, access, and resources
Facilitate projects from onboarding through technical transfer
Guide imaging strategy, data acquisition and annotation, classical vision, deep learning, multimodal models, 2D/3D analysis, edge inference, and workflow integration
Define visual dataset curation, labeling, annotation, quality control, lineage, versioning, and access-control practices
Establish approaches for class imbalance, scarce defects, weak labels, synthetic data, augmentation, and active learning
Define model-development baselines, evaluation protocols, acceptance thresholds, metrics, validation conditions, and error-analysis practices
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, including findings, confidence, evidence, recommended actions, review paths, escalation, and feedback capture
Provide technical guidance, mentor teams, and ensure reliable, measurable, traceable, and sustainable 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; 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
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

Competitive compensation
Discretionary annual bonus
Medical, dental, vision, and prescription drug coverage
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
Tax-advantaged 401(k) savings opportunity with company matching contributions and 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