Senior Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at Oredata Yazılım Anonim Şirketi. Istanbul.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and tune machine learning models for customer segmentation, recommendations, revenue forecasting and demand prediction
- Use Spark and PySpark to process large datasets from cloud storage and relational databases
- Prepare features, train and validate models, assess results, then optimize model settings
- Write scalable production code and add support for monitoring models and retraining them automatically
- Work with data engineering and platform teams to integrate models into the existing OpenShift environment
What you bring
- Strong practical Python and traditional machine learning experience, including Scikit-learn, XGBoost or LightGBM
- Hands-on Spark or PySpark experience processing large datasets and applying machine learning
- Practical work in customer segmentation, recommendation systems or forecasting
- Experience taking models and code into production, with feature engineering, evaluation and optimization
- Strong SQL skills and experience using relational databases
Who this fits
This senior role suits an engineer focused on building machine learning solutions for business use cases, rather than designing new platforms or infrastructure. The position is onsite in Istanbul.
From the employer
🚀 Ready to take your Machine Learning expertise to new heights?
About the Role
We are looking for an experienced Senior Machine Learning Engineer to contribute to large-scale Machine Learning and AI projects within the aviation industry.✈️
The ideal candidate will have strong expertise in traditional Machine Learning, hands-on experience with Apache Spark/PySpark, and a proven track record of developing and preparing ML models for production environments.
The role will focus entirely on developing Machine Learning solutions for business use cases such as Customer Segmentation, Recommendation Systems, Revenue Forecasting, and Demand Prediction, utilizing our existing technology and data infrastructure.
Key Responsibilities
- Design, develop, and optimize Machine Learning models to address complex business challenges.
- Develop and implement ML solutions for Customer Segmentation, Recommendation Systems, and Forecasting.
- Work with large-scale datasets using Apache Spark and PySpark, extracting data from both cloud-based storages and relational databases.
- Perform feature engineering, model training, validation, evaluation, and hyperparameter optimization.
- Develop clean, scalable, and production-ready Machine Learning code.
- Write robust code to support model monitoring and automated retraining processes.
- Evaluate and continuously improve model performance.
- Collaborate with Data Engineering and Platform teams to deploy and integrate ML solutions into the existing OpenShift environment (No new platform design/infrastructure setup is required).
Required Qualifications
- Strong hands-on experience in Python and traditional Machine Learning.
- Proficiency in ML libraries such as Scikit-learn, XGBoost, and LightGBM.
- Hands-on experience with Apache Spark / PySpark for large-scale data processing and Machine Learning.
- Solid understanding of supervised and unsupervised learning techniques, including Classification, Regression, and Clustering.
- Practical experience in at least one of the following areas: Customer Segmentation, Recommendation Systems, or Forecasting.
- Experience in feature engineering, model development, evaluation, and optimization.
- Experience developing production-ready ML models and code.
- Ability to develop code supporting model monitoring and automated retraining pipelines.
- Strong SQL skills and experience working with relational databases (e.g., Teradata, Oracle).
- Strong analytical thinking and problem-solving skills.
Nice to Have
- Experience with Spark MLlib.
- Familiarity working within OpenShift or container-based environments.
- Experience working with S3, Apache Iceberg format, and Feature Store architectures.
- Experience with ML lifecycle management and MLOps practices.
- Experience delivering large-scale Machine Learning solutions in production environments.