Solutions Architect, Energy OT, Industrial AI

NVIDIA

Energy OT / Industrial AI Solutions Architect bridging NVIDIA's AI with critical energy operations. Collaborating with customers to implement GPU-accelerated solutions for the energy sector.

Posted 7/18/2026full-timeTexas • 🇺🇸 United StatesMid-LevelSenior💰 $152,000 - $287,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing GPU-accelerated AI solutions and predictive maintenance for energy operations, leveraging advanced tools and frameworks. Possesses strong knowledge of power systems engineering and operational technology, with a focus on delivering high-value outcomes for customers.

Highest-signal resume keywords

GPU-Accelerated AI SolutionsPower Systems EngineeringPredictive MaintenancePython ProgrammingDeep Learning Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Energy ModelingSimulation ToolsOT/ICS Control SystemsAsset-Performance ManagementPredictive MaintenancePython ProgrammingDeep Learning Software ArchitectureGPU Parallel ProcessingDocker/ContainersKubernetes

Soft Skills

Excellent CommunicationProblem StructuringSelf-Direction

Tools & Technologies

OmniverseOpenUSDPhysicsNeMoJetson/IGXMetropolisMorpheusBlueFieldMATLAB/SimulinkPandapowerGridLAB-D

Industry Keywords

Energy OperationsOperational TechnologySCADADCSISO/RTO MarketsGrid OperationsRegulatory EnvironmentIndustrial Data ModelsAI WorkflowsHPC

Tech Stack

Tools & technologies

Cyber SecurityDockerKubernetesPython

About the role

Key responsibilities & impact

Partner with our industry and account teams to understand customer goals, strategies, and technical needs.
Define and deliver high-value, GPU-accelerated AI solutions for energy operations that meet these needs.
Develop industrial digital twins for plants, assets, and grids using Omniverse, OpenUSD, and PhysicsNeMo.
Implement predictive asset maintenance and asset-performance management solutions.
Deploy real-time AI at the edge for field, substation, and remote-site monitoring using Jetson/IGX and Metropolis.
Improve OT/ICS cybersecurity and secure remote access with Morpheus and BlueField.
Develop generative and agentic AI copilots for control-room and field workflows.
Perform forecasting and rapid simulation for power systems, subsurface, and process engineering.
Build industry direction by integrating NVIDIA technology into AI, HPC, and enterprise GPU and networking architectures for energy applications.
Strategically partner with flagship customers and industry-specific solution partners targeting our computing platform.
Collaborate primarily through virtual tools, with up to 20% travel required, and be empowered to find the best way to make our partners and customers successful.

Requirements

What you’ll need

MS or PhD in Electrical or Power Systems Engineering (or Chemical, Mechanical, or related engineering), Computer Science, Applied Mathematics, Physics, or equivalent experience.
Strong practical understanding of one or more energy sectors and their operational technology.
Experience includes OT/ICS and control systems like SCADA, EMS/ADMS/DERMS, DCS, and PI historians.
Familiar with operational tasks such as asset-performance management and predictive maintenance.
Experience with energy modeling and simulation tools — power-system tools such as pandapower, OpenDSS, GridLAB-D, MATPOWER/PYPOWER, PSS®E, PSCAD, PowerWorld, DIgSILENT PowerFactory, or MATLAB/Simulink.
Ability to transform energy and OT challenges into GPU-accelerated and AI workflows using NVIDIA's accelerated-computing and AI stack.
Familiarity with grid and industrial data models, standards, and protocols.
Familiarity with energy operations across the value chain — generation, grid operations and planning, interconnection, ISO/RTO markets, and field/surface operations — and the reliability and security regulatory environment.
At least 4 years working in power systems, industrial/OT engineering, data science, or software development, or in comparable positions, demonstrating strong Python ability and understanding of GPU parallel processing.
Experience with modern Deep Learning software architecture and frameworks and the Python data-science ecosystem.
Comfortable with modern application-deployment practices such as Docker/Containers and Kubernetes, including deployment to the edge.
Excellent communication skills — able to explain complex technical trade-offs to both engineers and executive partners.
Strong problem structuring and self-direction help drive ambiguous projects involving multiple interested parties to results.

Benefits

Comp & perks

equity
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