Predictive Maintenance ML Engineer: Time-Series & Edge

OpsSense Technologies Inc.; Lahore; Remote; PKR 2,790,000 - 4,464,000; Full time

Job summary

OpsSense Technologies Inc. is seeking an ML engineer to build predictive maintenance models that forecast bearing failures using multivariate sensor data. You will deploy models that maintenance planners can trust, prioritizing reliability over leaderboard metrics.

You will collaborate with firmware and backend teams to extract features at the edge and in the cloud, design rigorous evaluation, and integrate outputs into operator workflows with clear explanations for non-ML colleagues.

Qualifications

• 3+ years deploying ML in production on time-series or sensor data.
• Strong Python and standard ML stack, not just deep learning.
• Familiarity with signal processing (FFT) or quick to learn it.
• Rigorous evaluation with calibrated models and clear failure modes.
• Able to explain model behavior to non-ML colleagues.

Responsibilities

• Develop and productionize failure-prediction and anomaly-detection models on multivariate sensor time series.
• Build training and evaluation pipelines for noisy, imbalanced industrial data.
• Collaborate with firmware and backend teams on edge vs cloud feature extraction.
• Define evaluation metrics: precision-recall, lead-time, calibration for operations.
• Integrate model outputs into agent workflows with human-in-the-loop explanations.

Skills

Time-series ML

Anomaly detection

Python

Signal processing (FFT)

Model explainability

Tools

SciPy / NumPy

Job description

OpsSense Technologies Inc. is seeking an ML engineer to build predictive maintenance models that forecast bearing failures using multivariate sensor data. You will deploy models that maintenance planners can trust, prioritizing reliability over leaderboard metrics.

You will collaborate with firmware and backend teams to extract features at the edge and in the cloud, design rigorous evaluation, and integrate outputs into operator workflows with clear explanations for non-ML colleagues.