Job Description Summary

GE Vernova’s Power Conversion & Storage business is at the forefront of the energy transition. We are seeking several highly skilled 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 Description

Key Responsibilities

AI & 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.

Education

• Bachelor/Master’s degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field.

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.
• Hands-on experience with machine learning and deep learning frameworks such as TensorFlow and/or PyTorch, as well as classical ML tooling (e.g. scikit-learn, gradient boosting methods).
• Experience with time-series analysis, optimization methods, and data-driven model development.
• Practical experience with GenAI and their surrounding stacks (RAG, vector databases, A2A)
• Experience handling unstructured data; technical documents, specifications, reports; alongside structured and tabular data.
• Solid grounding in cloud services and architecture (Azure, AWS)
• Working knowledge of data engineering fundamentals: SQL, data pipelines, and structured/unstructured data handling.
• Familiarity with MLOps practices, model deployment, and integration into production environments is an advantage.

Domain Knowledge (Secondary)

• Sound knowledge of artificial intelligence, combined with a strong interest in emerging technologies and digital trends.
• Understanding of industrial processes, electrification, power systems, or related technical domains, or business processes, is an advantage.
• Awareness of the regulatory and governance context around AI (e.g. EU AI Act, GDPR) is a plus.
• Experience in innovation management and/or patent-related work is a plus.

Personal Attributes

• Proven ability to translate complex business and technical challenges into practical, scalable AI-driven solutions.
• Strong analytical and strategic thinking, with a high degree of self-motivation and a structured, goal-oriented working style.
• Strong documentation skills and attention to detail.
• Collaborative mindset with the ability to work effectively across functions and disciplines.
• Excellent written and verbal communication skills in English.

Additional Information