DevOps & MLOps Leader
gevernova
Job Description
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