Machine Learning Engineer (Mechanical Systems)
A role within the Travel & Transportation sector focused on fleet operations, vehicle reliability, and operational efficiency. The position combines mechanical engineering expertise with applied machine learning to deliver predictive-maintenance, telematics analytics, and optimization solutions for vehicle fleets and ground operations.Fully remote opportunity based in India supporting product and operations teams to reduce downtime, cut costs, and improve passenger experience through data-driven engineering.Role & Responsibilities
- Design and implement end-to-end ML solutions for fleet telematics and condition-based monitoring—data ingestion ➜ feature engineering ➜ model training ➜ deployment.
- Analyze sensor and vehicle telemetry (accelerometers, vibration, temperature, CAN/OBD-II) to build anomaly-detection and predictive-maintenance models that reduce unscheduled downtime.
- Work with mechanical engineering data (vibration signatures, thermodynamics metrics, component wear patterns) to translate physics insights into robust ML features and digital twins.
- Deploy and maintain production models using MLOps best practices: CI/CD pipelines, model versioning, monitoring, and automated retraining triggers.
- Collaborate with operations, field engineers, and product managers to prioritize use-cases, validate models in real environments, and close the loop with feedback data.
- Create documentation, run knowledge transfers, and establish testing/validation protocols for ML-driven mechanical diagnostics.
Skills & Qualifications
Must-Have
- Bachelor's degree in Mechanical Engineering, Mechatronics, Computer Science, or equivalent practical experience.
- 3+ years experience building ML models for time-series or sensor data using Python and libraries such as scikit-learn, pandas, NumPy.
- Hands-on experience with signal processing, feature extraction from vibration/telemetry, and anomaly-detection techniques.
- Proven experience deploying models to production (containerisation, REST APIs, cloud or edge inference) and familiarity with MLOps workflows.
- Practical mechanical engineering knowledge—failure modes, vibration analysis, basic CAD interpretation—that enables cross-functional collaboration with field teams.
- Strong version control (Git), unit testing, and data quality/ETL skills; comfortable with SQL and time-series databases.
Preferred
- Experience with deep-learning frameworks (TensorFlow or PyTorch) for sequence models or 1D convolutional architectures.
- Exposure to telematics platforms, CAN/OBD-II data, MQTT, and IoT ingestion pipelines.
- Familiarity with cloud platforms (AWS/GCP/Azure), Docker/Kubernetes, and monitoring tools (Prometheus/Grafana).
Benefits & Culture Highlights
- Fully remote role with flexible hours to support distributed teams across India.
- Hands-on ownership of high-impact projects that directly reduce fleet costs and improve service reliability.
- Collaborative environment bridging field engineering and data science—opportunities for cross-skilling and mentorship.
To apply: send a concise CV and portfolio or links to relevant projects demonstrating sensor-data ML, predictive-maintenance, or deployed ML systems. Candidates who can show real-world impact (downtime reduction, false-positive reduction, cost savings) will be prioritized.
Skills: machine learning,learning,maintenance
