Senior AI Architect - Computer Vision

GE Vernova, Inc.; South Carolina; On-site; USD 113,000 - 189,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

401(k) with company match

Tuition assistance

Paid holidays

Parental leave

Job summary

GE Vernova is seeking a Senior AI Architect for Computer Vision to lead the Wind Engineering Center of Excellence. You will establish shared frameworks for acquisition, labeling, training, evaluation, deployment, and monitoring across engineering initiatives, driving reusable patterns and governance for robust visual intelligence solutions.

You will guide imaging strategy, data acquisition/annotation, and develop 2D/3D, multimodal, and edge/cloud deployments, ensuring measurable inspection

Qualifications

• Minimum 8 years experience with data science and ML/DL in complex environments.
• Experience with Python and common data science frameworks (Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, XGBoost).
• Experience with time-series or industrial data; temporal validation and non-stationarity.

Responsibilities

• Lead the Computer Vision Center of Excellence for Wind Engineering, setting standards and reusable patterns.
• Define dataset governance, labeling taxonomies, and data leakage prevention.
• Establish model development and validation standards with clear metrics.
• Architect 2D/3D/Multimodal and Spatial AI solutions with robust uncertainty management.
• Design human-in-the-loop workflows and escalation paths for high-consequence observations.

Skills

Python

Data science frameworks

PyTorch

TensorFlow

XGBoost

Time-series data

Statistics

Model evaluation

Education

Bachelor's degree in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, OR Operations Research

Advanced degree preferred

Tools

Pandas

NumPy

Scikit-Learn

PyTorch

TensorFlow

XGBoost

Job description

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.

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.

Company Information

GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).

Relocation Assistance Provided: Yes. 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.

Benefits

• Medical, dental, vision, and prescription drug coverage
• Access to Health Coach from GE Vernova, a 24/7 nurse-based resource
• 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
• Tuition assistance
• Adoption assistance
• Paid parental leave
• Disability benefits
• Life insurance
• 12 paid holidays
• Permissive time off

Employment Terms

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.

Our Mission

Addressing the climate crisis is an urgent global priority and we take our responsibility seriously. That is our singular mission at GE Vernova: continuing to electrify the world while simultaneously working to help decarbonize it. If we want our energy future to be different…we must be different. Our mission is embedded in our name. We retain our treasured legacy, “GE,” in our name as an enduring and hard‑earned badge of quality and ingenuity. “Ver” / “verde” signal Earth’s verdant and lush ecosystems. “Nova,” from the Latin “novus,” nods to a new, innovative era of lower carbon energy that GE Vernova will help deliver. Together, we have The Energy to Change the World. www.gevernova.com