ML Engineer Predictive Maintenance
ML Engineer Predictive Maintenance
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Job Description
Job Title
ML Engineer Predictive MaintenanceJob Description
ML Engineer at DSP Predictive Maintenance
Hay Level: Hay 60
Job Location: Veghel
Vanderlande provides baggage handling systems for 600 airports globally moving over 4 billion pieces of baggage annually. For the parcel market our systems handle 52 million parcels daily. All these systems generate massive amounts of data. Do you see the challenge in building models and solutions that enable datadriven services including predictive insights using machine learning Would you like to contribute to Vanderlandes fastgrowing Technology Department and its journey to become more datadriven If so join our Digital Service Platform team!
Your Position
You will work as a Data Engineer with Machine Learning expertise in the Predictive Maintenance team. This hybrid and multicultural team includes Data Scientists Machine Learning Engineers Data Engineers a DevOps Engineer a QA Engineer an Architect a UX Designer a Scrum Master and a Product Owner.
The Digital Service Platform focuses on optimizing customer asset usage and maintenance impacting performance cost and sustainability KPIs by extending component lifetimes.
In your role you will:
- Participate in solution design discussions led by our Product Architect where your input as a Data Engineer with ML expertise is highly valued.
- Collaborate with IT and business SMEs to ensure delivery of highquality endtoend data and machine learning pipelines.
Your Responsibilities
Data Engineering
- Develop test and document data (collection and processing) pipelines for Predictive Maintenance solutions including data from (IoT) sensors and control components to our data platform.
- Build scalable pipelines to transform aggregate and make data available for machine learning models.
- Align implementation efforts with other backend developers across multiple development teams.
Machine Learning Integration
- Collaborate with Data Scientists to integrate machine learning models into production pipelines ensuring smooth deployment and scalability.
- Develop and optimize endtoend machine learning pipelines (MLOps) from data preparation to model deployment and monitoring.
- Work on model inference pipelines ensuring efficient realtime predictions from deployed models.
- Implement automated retraining workflows and ensure version control for datasets and models.
Continuous Improvement
- Contribute to the design and build of a CI/CD pipeline including integration test automation for data and ML pipelines.
- Continuously improve and standardize data and ML services for customer sites to reduce project delivery time.
- Actively monitor model performance and ensure timely updates or retraining as needed.
Your Profile
- Minimum 4 years experience building complex data pipelines and integrating machine learning solutions.
- Bachelors or Masters degree in Computer Science IT Data Science or equivalent.
- Handson experience with data modeling and machine learning workflows.
- Strong programming skills in Java Scala and Python (preferred for ML tasks).
- Experience with stream processing frameworks (e.g. Spark) and streaming storage (e.g. Kafka).
- Proven experience with MLOps practices including data preprocessing model deployment and monitoring.
- Familiarity with ML frameworks and tools (e.g. TensorFlow PyTorch MLflow).
- Proficient in cloud platforms (preferably Azure and Databricks).
- Experience with data quality management monitoring and ensuring robust pipelines.
- Knowledge of Predictive Maintenance model development is a strong plus.
What Youll Gain
- Opportunity to work at the forefront of datadriven innovation in a global organization.
- Collaborate with a talented and diverse team to design and implement cuttingedge solutions.
- Expand your expertise in data engineering and machine learning in a realworld industrial setting.
If you are passionate about leveraging data and machine learning to drive innovation wed love to hear from you!
Employment Type
Full-Time
