Senior Gen AI Applications Architect

Job Description Summary GE Vernova is accelerating the path to more reliable, affordable, and sustainable energy while helping customers power economies and deliver electricity that is vital to health, safety, security, and quality of life. Are you excited by the opportunity to electrify and decarbonize the world? We are seeking a highly skilled Senior AI/ML Applications Architect to lead the design, development, and deployment of advanced machine learning, generative AI, and agentic AI solutions.

This role combines deep technical expertise in AI/ML architecture with technical leadership and requires someone who can drive innovation from concept to production while guiding high-performing project teams. The role will advise product and engineering teams on architecture and solution approaches for generative AI and agentic AI, including the monitoring, observability, and guardrails required for responsible deployment. The role will also develop proofs of concept and enable production deployment of models across edge and cloud environments, including AWS or comparable cloud infrastructure.

The role reports to the Director of Data Analytics and AI Solutions in the Grid Automation CTO organization and collaborates closely with Grid Automation product lines, R&D teams, product management, and other functions to drive efficiency and innovation.

• 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 Grid Node 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.

• 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 Tensor Flow, 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…