MLOps Engineer (Enterprise AI Platform)

Summary: The MLOps Engineer builds, automates, and scales production-grade AI and Machine Learning solutions by working at the intersection of ML, Cloud Engineering, and DevOps. The role involves transforming ML models into reliable services, improving deployment processes, and collaborating with data scientists and engineer...

We are looking for an MLOps Engineer to help build, automate, and scale production-grade AI and Machine Learning solutions.

You will work at the intersection of Machine Learning, Cloud Engineering, and DevOps, helping transform ML models into reliable, scalable, and production-ready services.

This role offers the opportunity to work with modern cloud-native technologies, automate ML workflows, improve model deployment processes, and contribute to the development of enterprise AI infrastructure. You will collaborate with Data Scientists, ML Engineers, and Cloud Engineers to ensure that machine learning systems are efficiently deployed, monitored, and maintained.

• * *
• 2+ years of hands-on experience in MLOps, DevOps, Cloud Engineering, Machine Learning Engineering, or a related field, including practical exposure to ML deployment or ML workflows.
• Python: Good programming skills, including writing clean, maintainable, and testable code.
• Docker & Kubernetes: Practical experience with containerization and deploying applications in Kubernetes environments.
• Cloud Platforms: Hands-on experience with at least one major cloud provider: Azure, AWS, or GCP.
• CI/CD: Experience with automated build, test, and deployment pipelines using tools such as GitLab CI, GitHub Actions, or similar solutions.
• MLOps Fundamentals: Understanding of ML workflows, model deployment, experiment tracking, and model lifecycle management.
• Workflow Orchestration: Familiarity with at least one orchestration tool, such as Kubeflow Pipelines, Apache Airflow, or Argo Workflows.
• Infrastructure as Code: Practical knowledge of Terraform or similar IaC tools.
• Monitoring: Familiarity with monitoring and logging tools such as Prometheus, Grafana, or equivalent solutions.
• English: B2+ level, sufficient for technical discussions, documentation, and everyday collaboration in an international environment.

Nice to have:

• Experience with MLflow, including experiment tracking and model registry.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience with model serving technologies such as KServe, NVIDIA Triton Inference Server, vLLM, or ONNX Runtime.
• Knowledge of GitOps and deployment automation using Argo CD or Argo Rollouts.
• Experience with data drift detection, model monitoring, or ML observability tools.
• Familiarity with Apache Kafka, Apache Spark, or distributed data processing.
• Knowledge of GPU-based ML workloads, distributed training, or inference optimization.
• Experience with feature stores (e.g., Feast) or data versioning tools (e.g., DVC).
• Understanding of cloud security, IAM/RBAC, and secrets management.

Salary:

17 000 - 21 000 PLN + VAT (B2B)

### We offer you:

• Working with new technologies from the machine learning and top-notch software development area.
• Budget on self-development per year.
• Possibility to contribute to a variety of interesting projects.
• Internal workshops.
• Personal branding (articles, conference speaker, internal workshop leader).
• Flexible work hours.
• Remote work possibility.
• Chillout room / free beverages / team & company events.
• Friendly atmosphere.
• MultiSport.
• LuxMed.