We are looking for an experienced Senior DevOps / MLOps Engineer to join a high-impact project focused on the production deployment of an Anti-Churn Machine Learning system.
The goal of the project is to transition an analytical churn-prevention model from Proof of Concept (PoC) into a fully scalable, production-grade solution.
Key project pillars include:
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Prediction optimization – improving model performance using transactional and behavioral data (Oracle DWH)
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MLOps ecosystem development – full lifecycle automation (CI/CD) using Kubernetes, Apache Airflow, and MLflow
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Explainable AI (XAI) – implementation of SHAP to better understand customer behavior and decision factors
Key Responsibilities
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Design and implement stable runtime environments based on Kubernetes (RKE2 / Rancher)
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Build and maintain end-to-end CI/CD pipelines using GitLab CI/CD and Nexus
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Orchestrate complex data pipelines and batch workloads in Apache Airflow (KubernetesPodOperator)
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Implement enterprise-grade security standards, including:
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HashiCorp Vault integration
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Secrets management
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RBAC policies
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Configure and maintain monitoring and alerting for ML systems (Prometheus, Grafana, ELK)
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Collaborate closely with Data Engineering and Data Science teams to optimize container resource usage