Job Description SummaryGE Vernova's Power Conversion & Storage business is at the forefront of the energy transition. We are seeking several highly skilled Senior AI Engineers to join our teams to design, develop, and deliver AI-driven solutions that improve efficiency and decision-making across our business.
In this role, you will collaborate closely with domain experts and cross-functional teams to apply artificial intelligence, generative AI, and machine learning to real-world industrial challenges, helping accelerate innovation, productivity, and operational excellence.
A defining part of the role is technical judgment; choosing the right tool for each problem: classical machine learning, deep learning, GenAI, or pure software development when needed.
Job DescriptionAI & ML Solution Development & Integration- 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 modelling 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 the stakeholders needs, 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.
Technical Implementation & Architecture- 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.
Solution Delivery, Partner & Contractor Management- 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 & 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.
Data Strategy & Analytics- 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, labelling needs, access rights, confidentiality) and define strategies to close the gaps.
- 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.
Cross-Functional Collaboration- 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.
Continuous Innovation- 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.
Your ProfileEducation- Master's degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field.
Experience- Overall 8+ Years Experience.
- Several years (2 to 4) of professional experience in artificial intelligence, machine learning, data science, software engineering, or a comparable technical role.
- Experience developing and deploying AI/ML solutions in industrial, technical, or other complex operational environments is preferred.
Technical Expertise- Strong programming skills in Python, C++, or similar languages, and proficiency with modern development environments such as VS Code.
Additional InformationRelocation Assistance Provided: Yes
