This engineering role focuses on designing, developing, and delivering AI-driven solutions to enhance efficiency and decision-making within the Power Conversion & Storage business. The Staff AI Engineer will collaborate with domain experts and cross-functional teams, applying various AI and machine learning techniques to industrial challenges, with a strong emphasis on technical judgment in tool selection.
Responsibilities
Design, develop, and implement AI solutions, including generative AI, machine learning models, neural networks, and optimization algorithms, to improve business process efficiency and effectiveness
Select the appropriate modeling approach for each problem and articulate the trade-offs behind that choice
Own the machine learning lifecycle: dataset construction, metrics definition, acceptance criteria in accordance with stakeholder needs, and evaluation strategies
Translate business and operational needs into scalable AI-enabled tools, applications, and workflows
Support the deployment and integration of AI/ML models into existing business and technical systems, software environments, and products where applicable, including monitoring for drift, performance degradation, and running cost
Collaborate with domain experts to identify high-value use cases and define technical requirements for AI solutions
Define and document the solution architecture end to end: from data sources to the integration with the existing enterprise and technical IT landscape
Design cloud-ready and on-premises solutions aligned with company IT, cybersecurity, and data-governance standards
Integrate AI/ML capabilities into hardware, software, and business process ecosystems in a way that supports reliability, usability, and maintainability
Contribute to the development of robust, production-ready AI solutions suitable for industrial environments
Write clear technical specifications, statements of work, and acceptance criteria for work delivered by external contractors, software vendors, or internal digital teams
Contribute to supplier, platform, and tool selection through structured technical evaluation, benchmarking, and proof-of-concept comparison
Steer and review the work of internal and external partners: technical follow-up, design reviews, code and model reviews, quality gates, and acceptance testing; remaining the technical owner and guardian of the delivered solution
Ensure solutions remain maintainable after handover through documentation, knowledge transfer, and clearly assigned ownership, so that delivered tools do not become orphaned
Lead or support the collection, processing, structuring, and analysis of large-scale operational and business data
Assess data readiness ahead of any development (availability, quality, labeling needs, access rights, confidentiality) and define strategies to close the gaps
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Identify patterns, trends, and performance improvement opportunities using advanced analytics and AI methods
Develop data-driven solutions such as predictive maintenance, anomaly detection, quality and performance prediction, forecasting, cost analysis, document and requirement analysis, and knowledge support tools
Work closely with technical, operational, business, and IT teams to ensure AI solutions meet business and industry requirements for safety, reliability, performance, and scalability
Communicate technical concepts clearly to both technical and non-technical stakeholders
Help align AI initiatives with business priorities, operational goals, and constraints
Support end-user adoption: training, onboarding, feedback loops, and measurement of the benefits realized once the solution is live
Evaluate emerging technologies such as edge AI, synthetic data, reinforcement learning, and large language models for industrial and business applicability
Stay current with developments in AI, machine learning, and digital tools, and recommend practical adoption opportunities
Maintain an active technology watch on the AI tooling landscape and filter it, distinguishing capability gains from hype before proposing adoption
Contribute to building an innovation-oriented culture through knowledge sharing, experimentation, and continuous improvement
Requirements
Master’s degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field
Overall 8+ years of experience
2 to 4 years of professional experience in artificial intelligence, machine learning, data science, software engineering, or a comparable technical role
Strong programming skills in Python, C++, or similar languages, and proficiency with modern development environments such as VS Code
Nice to have:
Experience developing and deploying AI/ML solutions in industrial, technical, or other complex operational environments
Important Details
Relocation Assistance Provided: Yes
About the Company
GE Vernova’s Power Conversion & Storage business is at the forefront of the energy transition, seeking to improve efficiency and decision-making across its operations through AI-driven solutions.
Building on more than 130 years of experience, GE Vernova is a purpose-built company, focused on our mission to electrify and decarbonize the world.
