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Staff AI Engineer (Backend Frameworks)
GE Vernova - PROD
5 hours ago
Full-time
On-site
Staff Software Engineer
The power grid is changing — and the software behind it needs to change faster. As a Staff Software Engineer on GE Vernova's Applied Grid Innovation team, you'll build the backend platforms and artificial intelligence (AI) agent frameworks that make next-generation grid management possible. Your work will directly shape how grid operators make decisions, automate complex workflows, and maintain reliable energy delivery at scale. This is a senior individual contributor role for an engineer who thrives on system architecture challenges and wants their code running in production environments where reliability isn't optional. Design and build distributed backend systems using Python, Java, C++, or Go — creating scalable microservices, APIs, and event-driven architectures that integrate with operational grid systems including Supervisory Control and Data Acquisition (SCADA), Phasor Measurement Unit (PMU) data streams, and time-series databases. Architect AI agent frameworks that orchestrate machine learning (ML) models, rule engines, simulation engines, and enterprise grid services — enabling intelligent automation and decision support in control room environments. Develop AI/ML production infrastructure, including model lifecycle management, inference services, agent coordination layers, and real-time feedback loops for Advanced Energy Management Systems (AEMS), Advanced Distribution Management Systems (ADMS), and Wide Area Monitoring Systems (WAMS). Lead code quality and engineering excellence, including automated testing, Continuous Integration/Continuous Deployment (CI/CD) pipeline development, performance tuning, and system observability. Collaborate across disciplines — working alongside data scientists, power systems engineers, and DevOps teams to bring AI-driven solutions from prototype to production in mission-critical environments. Contribute to architecture decisions with a focus on scalability, fault tolerance, cybersecurity, and suitability for on-premises control center deployments. You're an experienced backend engineer who combines deep systems architecture expertise with hands-on experience delivering AI/ML infrastructure in production — and you're ready to apply that expertise to one of the world's most critical infrastructure domains. Required 5+ years of professional experience in backend or distributed systems development Proficiency in Python and at least one additional backend language (Java, C++, or Go) Proven experience designing microservices, REST/gRPC APIs, and event-driven architectures Hands-on experience building AI/ML production systems — including model serving, inference pipelines, or agent-based orchestration frameworks Experience with AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines Strong understanding of software architecture, concurrency, system performance, and security principles Preferred Experience developing software for on-premises, mission-critical, or operational technology (OT) control environments Working knowledge of Energy Management Systems (EMS), Distribution Management Systems (DMS), WAMS, SCADA, or power system operations Familiarity with time-series data systems and high-throughput data processing pipelines Education A formal education and subsequent Bachelor's, Master's, or PhD in Computer Science, Software Engineering, or a related field is nice to have, but we are most interested in your total experience and professional achievements.
The power grid is changing — and the software behind it needs to change faster. As a Staff Software Engineer on GE Vernova's Applied Grid Innovation team, you'll build the backend platforms and artificial intelligence (AI) agent frameworks that make next-generation grid management possible. Your work will directly shape how grid operators make decisions, automate complex workflows, and maintain reliable energy delivery at scale. This is a senior individual contributor role for an engineer who thrives on system architecture challenges and wants their code running in production environments where reliability isn't optional. Design and build distributed backend systems using Python, Java, C++, or Go — creating scalable microservices, APIs, and event-driven architectures that integrate with operational grid systems including Supervisory Control and Data Acquisition (SCADA), Phasor Measurement Unit (PMU) data streams, and time-series databases. Architect AI agent frameworks that orchestrate machine learning (ML) models, rule engines, simulation engines, and enterprise grid services — enabling intelligent automation and decision support in control room environments. Develop AI/ML production infrastructure, including model lifecycle management, inference services, agent coordination layers, and real-time feedback loops for Advanced Energy Management Systems (AEMS), Advanced Distribution Management Systems (ADMS), and Wide Area Monitoring Systems (WAMS). Lead code quality and engineering excellence, including automated testing, Continuous Integration/Continuous Deployment (CI/CD) pipeline development, performance tuning, and system observability. Collaborate across disciplines — working alongside data scientists, power systems engineers, and DevOps teams to bring AI-driven solutions from prototype to production in mission-critical environments. Contribute to architecture decisions with a focus on scalability, fault tolerance, cybersecurity, and suitability for on-premises control center deployments. You're an experienced backend engineer who combines deep systems architecture expertise with hands-on experience delivering AI/ML infrastructure in production — and you're ready to apply that expertise to one of the world's most critical infrastructure domains. Required 5+ years of professional experience in backend or distributed systems development Proficiency in Python and at least one additional backend language (Java, C++, or Go) Proven experience designing microservices, REST/gRPC APIs, and event-driven architectures Hands-on experience building AI/ML production systems — including model serving, inference pipelines, or agent-based orchestration frameworks Experience with AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines Strong understanding of software architecture, concurrency, system performance, and security principles Preferred Experience developing software for on-premises, mission-critical, or operational technology (OT) control environments Working knowledge of Energy Management Systems (EMS), Distribution Management Systems (DMS), WAMS, SCADA, or power system operations Familiarity with time-series data systems and high-throughput data processing pipelines Education A formal education and subsequent Bachelor's, Master's, or PhD in Computer Science, Software Engineering, or a related field is nice to have, but we are most interested in your total experience and professional achievements.