Why This Role Stands Out
This hybrid role offers a fantastic opportunity to leverage your machine learning and AI expertise to drive impactful solutions in asset management and predictive maintenance, fostering significant career growth. You'll thrive here if you possess strong Python and SQL skills, enjoy building data-driven models, and are eager to contribute to innovative projects within a collaborative environment. Apply now to join a forward-thinking company and advance your skills in a dynamic field.
Quick Overview
Job Description
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.
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