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Machine Learning / AI Engineer - Remote

Techaffinity ConsultingUnited States🇺🇸United StatesPosted Oct 9, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Remote
Location
United States
Posted
21 hours ago
SQLMachine LearningScikit-learnPyTorchPythonTensorFlow

Job Description

Title: 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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