Job Description Summary

GE 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 Description

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.

Your Profile

Education

Master's degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field.

Experience

Overall 8+ Years of 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

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

Relocation Assistance Provided: Yes

Staff AI engineer