Senior AI Architect - Computer Vision

GE Vernova•Schenectady, NY

4d•$113,200 - $188,800

About The Position

The Senior AI Architect for Computer Vision leads the Computer Vision Center of Excellence for Wind Engineering. The role establishes shared frameworks, standards, and reusable practices for reliable visual intelligence across engineering initiatives, and manages computer vision platforms, project workspaces, access, and resources. It facilitates projects from onboarding through technical transfer and guides imaging strategy, data acquisition and annotation, computer vision methods, multimodal models, 2D/3D analysis, edge inference, and integration into engineering and field workflows. Through technical guidance, team mentorship, and consistent validation and governance, the role helps ensure vision solutions become reliable engineering products with measurable inspection performance, traceable datasets, and sustainable deployment across manufacturing, laboratory, turbine, drone, robotic, and service contexts. It establishes reusable approaches for computer vision development and deployment; dataset governance; model validation; spatial and multimodal AI; and human-in-the-loop inspection workflows.

Requirements

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 in a quantitative field.
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 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 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 design choices for cameras, drones, robots, inspection devices, and engineering applications.
Define dataset curation, labeling, annotation quality, disagreement resolution, lineage, versioning, and access-control standards.
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 covering intended use, coverage, limitations, provenance, and known failure modes.
Establish baseline methods, evaluation protocols, acceptance thresholds, and error-analysis practices for computer vision systems.
Set task-appropriate evaluation metrics and define validation across varying lighting, weather, backgrounds, camera configurations, product variants, sites, and operating conditions.
Ensure model comparisons account for engineering consequences and workflow economics, not just aggregate benchmark results.
Guide architectures combining visual, spatial, engineering, and inspection-history data 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 managed explicitly 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 can improve datasets and future model versions.
Ensure AI augments accountable engineering decisions without obscuring required inspection evidence.
Provide technical guidance and mentorship, conduct senior technical reviews, and communicate complex methods clearly.

Benefits

Medical insurance
Dental insurance
Vision insurance
Prescription drug coverage
24/7 nurse-based Health Coach resource
Employee Assistance Program with confidential assessment, counseling, and referral services
401(k) retirement 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
Discretionary annual bonus
Relocation assistance