Machine Learning / AI Engineer
Yale University
- Job
- 37407
- Posted
- Location
- Remote
- Work type
- Full Time
- Tax terms
- W2, Yearly
- Experience
- Experience open
- Openings
- 1 opening
Skills
- Maintenance Planning
- Data Analysis
- Analytics
- Forecasting
- Enterprise Asset Management
- Asset Management
- Artificial Intelligence
- Data Science
- Python
- SQL
- scikit-learn
- TensorFlow
- PyTorch
- Machine Learning (ML)
- Algorithms
- Predictive Analytics
- Database
About the job
Machine Learning / AI Engineer
Experience: 4+ years
Location: Remote
Job Summary:
We are seeking a Machine Learning / AI Engineer to develop data-driven solutions supporting asset management, predictive maintenance, fleet operations, and maintenance planning. The ideal candidate will have strong hands-on experience in Python, machine learning, data analytics, and enterprise data environments.
Experience with asset management, fleet maintenance, or transportation systems is highly preferred.
Responsibilities:
- Develop and implement machine learning and AI solutions for predictive maintenance, asset failure prediction, anomaly detection, forecasting, and asset performance optimization.
- Analyze work order, asset, maintenance, and operational data to identify trends, failure patterns, and improvement opportunities.
- Develop models and analytics supporting condition-based maintenance and asset replacement decisions.
- Develop forecasting models to support maintenance and operational decision-making.
- Build and integrate data pipelines and AI/ML solutions with enterprise applications, databases, APIs, and reporting platforms.
- Evaluate model performance and translate analytical results into actionable recommendations for business and maintenance teams.
- Collaborate with EAM functional and technical teams to develop and implement AI/ML solutions aligned with asset management and maintenance requirements.
Required Qualifications:
- 4+ years of experience in Machine Learning, AI, Data Science, or a related field.
- Strong hands-on experience with Python and SQL.
- Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
- Experience with data preparation, feature engineering, model development, validation, and deployment.
- Strong understanding of machine learning algorithms, predictive analytics, and model evaluation.
- Experience working with databases, APIs, and enterprise data environments.