Senior Gen AI Applications Architect
Role Overview
Roles and Responsibilities
• 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. Ensure performance across edge and cloud deployment environments.
• Establish architectural standards, best practices, and technical guidelines for AI/ML development across the CTO organization in collaboration with GE Vernova AI/ML partners.
• Build a strong technical foundation 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, including model selection, orchestration patterns, and integration strategies.
• Define and implement monitoring, observability, and guardrails for generative AI and agentic AI solutions, including controls for safety, security, bias, hallucination mitigation, cost, and performance.
• 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 relevant 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.
Key Skills for This Role
Full Job Posting
Roles and Responsibilities
- 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. Ensure performance across edge and cloud deployment environments.
- Establish architectural standards, best practices, and technical guidelines for AI/ML development across the CTO organization in collaboration with GE Vernova AI/ML partners.
- Build a strong technical foundation 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, including model selection, orchestration patterns, and integration strategies.
- Define and implement monitoring, observability, and guardrails for generative AI and agentic AI solutions, including controls for safety, security, bias, hallucination mitigation, cost, and performance.
- 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 relevant 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.
Required Qualifications
- 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.
Desired Characteristics
Proven expertise with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn and 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, including safety, security, bias, hallucination, cost, and performance controls.
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, and deployment technologies such as Docker and Kubernetes.
Experience designing solutions across the complete AI/ML lifecycle: data acquisition, data quality and engineering, model selection and evaluation, tuning, testing, deployment, monitoring, maintenance, and evolution.
Strong background in edge computing, industrial IoT, real-time processing, and cloud platforms such as 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++, along with 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 while balancing innovation, implementation constraints, security, maintainability, and business requirements.
Understanding of industrial IoT, edge-computing requirements, and real-time data processing in critical-infrastructure environments.
**For candidates applying to a Canadian-based position, the pay range for this position is between
- $126,000.00 - 189,000.00 CAD Annual. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.
Relocation Assistance Provided
- No
About GE Vernova
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