AI Engineer

GE Vernovas 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.

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/Masters 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.