Job Description Summary

GE Vernova is seeking a technical and people leader to build, scale, and lead a global DevOps and MLOps capability that enables advanced industrial R&D, digital engineering, and AI driven innovation across the energy value chain.

This role will design and operate secure, reliable, and scalable DevOps and MLOps platforms that support agile development of software, controls, digital twins, analytics, and machine learning solutions used in mission critical, regulated energy environments. The successful candidate will partner and collaborate closely with engineering, data science, and product teams to accelerate innovation while meeting the highest standards of quality, safety, cybersecurity and compliance.

Job Description

Major Responsibilities:

DevOps & MLOps Strategy

• Define and execute the global DevOps / MLOps strategy aligned with GE Vernova’s R&D and Engineering objectives
   
• Architect and govern industrial‑grade platforms supporting:
  • Software and controls development
  • AI/ML model lifecycle management
  • Simulation, digital twins, and advanced analytics
     
• Balance speed of innovation with engineering rigor, traceability, and regulatory requirements
   

Platform & Architecture Leadership

• Design and oversee platforms for:
  • CI/CD and CI/CT pipelines (including testing for industrial software and embedded systems where applicable)
  • On-Prem, cloud, hybrid, and edge computing environments
  • Secure ML training, deployment, monitoring, and retraining pipelines
  • Drive adoption of infrastructure as code, automation, observability, and platform engineering best practices
  • Evaluate, select, and integrate new tools and technologies that improve developer and data scientist productivity
     

MLOps for Industrial AI

• Enable scalable, governed MLOps capabilities supporting:
  • Predictive maintenance
  • Asset performance optimization
  • Grid analytics and energy transition solutions
     
• Partner with data science and domain engineering teams to:
  • Standardize ML workflows and model governance
  • Ensure model explainability, traceability, and lifecycle management
  • Support deployment in both cloud and edge environments

• People & Organizational Leadership
  • Build, lead, and develop a globally distributed inclusive and diverse DevOps and MLOps engineering team
  • Establish clear technical roles, career paths, and employee-led development plans
  • Foster a culture of engineering excellence, continuous improvement, safety, and accountability
  • Lead global hiring, onboarding, performance management and talent development
     

Security, Compliance & Quality

• Ensure platforms comply with:
  • Cybersecurity standards
  • Data governance and privacy requirements
  • Industry and regulatory expectations relevant to energy and industrial systems
• Embed security, quality, and reliability into all DevOps and MLOps pipelines (“secure by design”)
   

Stakeholder Partnership & Influence

• Act as a strategic partner to:
  • R&D and Engineering leadership
  • R&D and AI teams, Cybersecurity, IT, and Enterprise Architecture
• Translate complex engineering and business requirements into robust, scalable platform solutions
• Communicate technical strategy and trade-offs effectively to senior leadership
   

Basic Qualifications:
 

Experience

• Advanced degree in Engineering, Computer Science, or related field
• 10+ years of experience in DevOps, platform engineering, or cloud infrastructure
• 5+ years leading global, multidisciplinary engineering teams
• Proven experience supporting industrial, product, or R&D engineering organizations
• Hands-on experience implementing MLOps in production, preferably for industrial AI use cases
   

Technical Expertise

• Strong experience with:
  • CI/CD tools and automation, including test automation
  • Cloud platforms (AWS, Azure, GCP) and hybrid architectures
  • Containers and orchestration (Docker, Kubernetes)
  • Infrastructure as Code (Terraform, ARM, CloudFormation, etc.)
  • Ability to audit teams adopting DevOps / MLOps for compliance against a maturity framework
• Practical knowledge of MLOps frameworks and platforms (e.g., MLflow, Kubeflow, Azure ML, SageMaker)
• Understanding of industrial cybersecurity, reliability, and compliance constraints
• Agile development teams and awareness of NPI processes and Scaled Agile Framework
   

Leadership & Collaboration

• Proven ability to lead and scale high‑performing global teams
• Strong coaching, mentoring, and talent development skills
• Excellent communication skills across technical and non‑technical audiences
• Ability to influence without authority in a complex matrix organization
   

Desired

• Experience in energy, power systems, renewables, grid, or heavy industrial domains
• Exposure to embedded software, controls, or edge AI deployments
• Experience with digital twins, simulation platforms, or physics‑based models
   

​Whats Success Looks Like

• R&D and engineering teams can rapidly develop, test, deploy, and operate industrial software and AI solutions
• TTM and Quality are improved by adoption of DevOps / MLOps systems, measured by KPI for continuous improvement
• DevOps and MLOps platforms are secure, standardized, and trusted across the organization
• AI and digital innovation move faster while maintaining engineering quality and regulatory compliance
• Teams are engaged, growing, and recognized as strategic enablers of GE Vernova’s energy transition mission

Additional Information