Artificial Intelligence Engineer

HYRED; Singapore; On-site; SGD 90,000 - 180,000; Full time

Job summary

HYRED is seeking an AI Engineer in Singapore to develop and deploy machine-learning solutions from real-world industrial sensor data. You will build models for predictive maintenance and fault diagnosis, preprocess large time-series datasets, and translate research into production-ready workflows.

This hands-on role emphasizes putting models into production to improve asset reliability and operational performance, with close collaboration to data pipelines and engineering teams.

Qualifications

• Bachelor’s/Master’s degree in CS/AI/DS or related field.
• At least 3 years of hands-on ML model development and deployment.
• Experience with time-series data and industrial sensor datasets.

Responsibilities

• Develop ML and DL models for predictive maintenance, anomaly detection and fault diagnosis.
• Preprocess and analyze large-scale time-series and industrial sensor data.
• Work with signals such as vibration and temperature for model inputs.
• Build scalable models for real-time or low-latency prediction.
• Monitor model performance on production data and iterate.
• Collaborate with engineering to improve data pipelines and model accuracy.
• Design experiments and validate hypotheses with machine data.
• Translate academic research into production-ready approaches.
• Document models, methods and performance for stakeholders.

Skills

Machine learning

Time-series analysis

Python/R/Java

Signal processing

Noisy real-world data handling

Education

Bachelor's or Master's in Computer Science/AI/Data Science or related

Tools

TensorFlow

PyTorch

Scikit-learn

AWS

GCP

Azure

Job description

We are supporting a Singapore-headquartered industrial AI company building technology for predictive maintenance, machine health monitoring and industrial operations.

They are looking for a AI Engineer to develop and deploy machine-learning solutions using real-world sensor and time-series data from industrial equipment.

This is a hands-on role for someone who enjoys taking models beyond experimentation and putting them into production environments where they directly influence asset reliability and operational performance.

What you’ll work on

• Develop machine-learning and deep-learning models for predictive maintenance, anomaly detection and fault diagnosis.
• Analyse and preprocess large-scale time-series and industrial sensor datasets.
• Work with physical signals including vibration, temperature, current, sound and pressure.
• Build and deploy scalable models for real-time or low-latency prediction.
• Monitor model behaviour and continuously improve performance using production data.
• Work with engineering teams to improve data pipelines and model accuracy.
• Design experiments and use machine data to validate hypotheses and benchmark models.
• Translate relevant academic research into practical, production-ready approaches.
• Document models, methodologies and performance for internal and stakeholder use.

What we’re looking for

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Business Analytics or a related discipline.
• At least 3 years of experience developing and deploying analytical or machine-learning models.
• Strong experience in:
• Machine learning
• Time-series analysis
• Proficiency in Python, R or Java.
• Experience with frameworks such as TensorFlow, PyTorch or Scikit-learn.
• Familiarity with AWS, GCP or Azure.
• Understanding of signal processing and industrial sensor data.
• Ability to work with noisy, high-dimensional real-world datasets.