Senior DevOps Engineer /BP

Responsibilities

• Design and implement stable runtime environments based on Kubernetes (RKE2 / Rancher)
• Build and maintain end-to-end CI/CD pipelines using GitLab CI/CD and Nexus
• Orchestrate complex data pipelines and batch workloads in Apache Airflow (KubernetesPodOperator)
• Implement enterprise-grade security standards, including: HashiCorp Vault integration
• Secrets management
• RBAC policies
• Configure and maintain monitoring and alerting for ML systems (Prometheus, Grafana, ELK)
• Collaborate closely with Data Engineering and Data Science teams to optimize container resource usage
• Must-Have Requirements
• 5+ years of experience in DevOps or System Engineering
• Expert-level knowledge of Kubernetes (administration, networking, storage), especially in on-premise environments
• Hands-on experience with MLOps automation and Apache Airflow orchestration
• Proven track record delivering secure solutions in regulated industries (FinTech, Banking, Insurance)
• Strong experience with CI/CD tools and containerization (Docker)
• Solid knowledge of MLflow (Tracking, Model Registry, Serving)

Requirements

• Experience with Infrastructure as Code tools (Terraform, Ansible)
• Familiarity with air-gapped environments (restricted internet access setups)
• We hereby inform you that Inetum Polska sp. z o.o. has implemented an internal reporting (whistleblowing) procedure. The content of the procedure and the possibility to submit an internal report are available at:
• https://inetum.whispli.com/speakup?locale=pl

Additional Information

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: Prediction optimization - improving model performance using transactional and behavioral data (Oracle DWH) MLOps ecosystem development - full lifecycle automation (CI/CD) using Kubernetes, Apache Airflow, and MLflow Explainable AI (XAI) - implementation of SHAP to better understand customer behavior and decision factors

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