Senior Machine Learning Engineer
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Before you apply
LocationIstanbul, İstanbul, Turkey
Typefull-time
SenioritySenior
DepartmentTHY
Company size4+ people
PostedOct 14, 2026 · 3w ago
Verified live10h ago
At a glanceSummarised by Seekless from the posting.
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
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
Skills
Python
Scikit-learn
XGBoost
LightGBM
Apache Spark
PySpark
Spark MLlib
OpenShift
S3
Apache Iceberg
SQL
Teradata
Oracle
About the role
🚀 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
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Design, develop, and optimize Machine Learning models to address complex business challenges.
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Develop and implement ML solutions for Customer Segmentation, Recommendation Systems, and Forecasting.
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Work with large-scale datasets using Apache Spark and PySpark, extracting data from both cloud-based storages and relational databases.
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Perform feature engineering, model training, validation, evaluation, and hyperparameter optimization.
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Develop clean, scalable, and production-ready Machine Learning code.
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Write robust code to support model monitoring and automated retraining processes.
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Evaluate and continuously improve model performance.
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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
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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
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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.
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