Senior Gen AI Applications Architect

GE Vernova

. Design and architect scalable AI/ML solutions, including generative AI and agentic AI applications, for grid automation, digitalization, product-line use cases, and business efficiency .

Posted 10/23/2026full-timeRemote • CanadaSenior💰 CA$126,000 - CA$189,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and architecting scalable AI/ML solutions, particularly in generative AI and agentic AI applications, while applying best practices in cloud infrastructure and MLOps. Proficient in developing production-quality systems and integrating AI/ML capabilities into existing platforms for enhanced business efficiency.

Highest-signal resume keywords

Generative AI Solution ArchitectureAgentic AI FrameworksCloud MLOps ServicesMachine Learning FrameworksAI/ML Lifecycle Management

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonTensorFlowPyTorchDockerKubernetesAWSAzureGoogle Cloud PlatformMATLABCI/CD

Tools & Technologies

GE GridNodeDataiku CloudAmazon SageMakerAzure Machine LearningGoogle Vertex AI

Industry Keywords

AI/ML DevelopmentGrid AutomationIndustrial IoTEdge ComputingReal-Time Processing

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformIoTKubernetesMicroservicesNoSQLPythonPyTorchScikit-LearnSQLTensorflowC++

About the role

Key responsibilities & impact

Design and architect scalable AI/ML solutions, including generative AI and agentic AI applications, for grid automation, digitalization, product-line use cases, and business efficiency
Establish architectural standards, best practices, and technical guidelines for AI/ML development across the CTO organization
Build technical foundations using modular and microservices architectures, cloud and edge patterns, API-first and privacy-by-design principles, containerization, orchestration, autoscaling, infrastructure as code, CI/CD, data and MLOps pipelines, and collaborative experimentation environments
Design and deploy solutions on GE GridNode and other edge platforms using container and microservices principles
Develop strategies to optimize model performance in production
Advise project and product teams on architecture and solution approaches for generative AI and agentic AI use cases
Define and implement monitoring, observability, and guardrails for generative AI and agentic AI solutions
Guide teams in building production-quality systems and reusable frameworks that are scalable, reliable, and maintainable
Collaborate with cross-functional teams to integrate AI/ML capabilities into existing platforms and develop new intelligent product and business-efficiency solutions
Monitor advances in AI/ML, generative AI, agentic AI, and energy-system technologies and translate developments into practical recommendations
Evaluate AI/ML tools, platforms, methodologies, and vendor solutions for grid-automation challenges and business opportunities
Design and execute proofs of concept to validate feasibility and inform production decisions
Report to the Director of Data Analytics and AI Solutions in the Grid Automation CTO organization

Requirements

What you’ll need

Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or a related technical field
Minimum of 7 years of hands-on experience in software engineering, AI/ML development, and/or solution architecture
Demonstrated hands-on experience building and architecting generative AI and agentic AI solutions and applications
Experience architecting and implementing generative AI and agentic AI solutions on AWS or comparable cloud infrastructure such as Microsoft Azure or Google Cloud Platform
Proven expertise with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn
Experience with generative AI technologies including large and small language models, diffusion models, and GANs
Experience with agentic AI frameworks and architectures, including multi-agent orchestration, tool and function calling, retrieval-augmented generation, agent memory, and planning
Experience implementing monitoring, observability, and guardrails for generative AI and agentic AI systems
Experience applying AI/ML frameworks, AI/MLOps, and CI/CD across cloud-native and on-premises development and deployment in operational technology or industrial automation environments
Experience developing and deploying ML models using cloud MLOps services such as Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Dataiku Cloud, or equivalent
Hands-on experience developing and testing AI/ML algorithms and experience with grid or physics models using MATLAB, PSCAD, PSS/E, DIgSILENT PowerFactory, or equivalent tools
Experience with DevOps, data pipelines, model registries, Docker, and Kubernetes
Experience designing solutions across the complete AI/ML lifecycle
Strong background in edge computing, industrial IoT, real-time processing, and AWS, Microsoft Azure, or Google Cloud Platform
Experience with graph databases, SQL and NoSQL databases, and Microsoft Access
Proficiency in Python and at least one additional language such as C# or C++
Experience in scientific programming or simulation tools such as MATLAB or R
Experience with time-series analysis, signal processing, load forecasting, or predictive modeling relevant to energy systems and grid operations
Proven record of delivering complex AI/ML projects from conception through production deployment
Ability to apply research insights to real-world business problems and commercial solutions
Understanding of industrial IoT, edge-computing requirements, and real-time data processing in critical-infrastructure environments

Benefits

Comp & perks

Discretionary annual bonus
Relocation Assistance Provided: No