Mid/Senior MLOps Engineer (GCP / Azure / Kubernetes) 100% Remote

☁️ About Datumo

Datumo is a Big Data & Cloud company specializing in data platforms, data engineering, cloud modernization and practical AI solutions.

We work with large organizations and enterprise-scale data environments. Our clients include companies such as Żabka Polska, G2A and LPP SA.

We are one of only 10 Google Cloud Managed Partners in Poland. We are also an Azure Partner and Snowflake Partner, with strong experience in GCP, Azure, Snowflake and Databricks ecosystems.

We are not a general software house doing everything for everyone. We focus on data.

💻 What You Will Work On

You will contribute to MLOps systems that support real cloud and data platforms. Your work may include:
- turning model experiments into production-ready services,
- building deployment and automation processes,
- setting up monitoring and observability for model-serving environments,
- improving reliability, scalability and maintainability of MLOps platforms,
- working with Kubernetes in cloud environments,
- supporting governance and security requirements.

👀 What We Expect

Must-Have

• 3+ years of commercial experience in MLOps, ML Engineering, DevOps, Data Engineering, cloud or backend engineering, including production ML workloads.

• Experience with Azure or Google Cloud.

• Strong Python skills, including testing, packaging and dependency management.

• Hands-on experience with Docker and Kubernetes.

• Practical knowledge of CI/CD, Git workflows and deployment automation.

• Understanding of the ML lifecycle: tracking, versioning, deployment, inference, monitoring and rollback.

• Basic knowledge of ML concepts, including metrics, reproducibility and data or model drift.

• English at B2+ level and communicative Polish.

Nice-to-Have

• Experience with Vertex AI, Azure Machine Learning, Databricks or Snowflake.

• Knowledge of MLflow, Kubeflow or similar platforms.

• Experience with Airflow, Prefect or Dagster.

• Infrastructure-as-Code experience, preferably Terraform or Bicep.

• Experience with Helm and Kubernetes deployment automation.

• Experience with batch or real-time inference and model serving.

• Knowledge of model monitoring, drift detection and automated retraining.

• Experience with Prometheus, Grafana or cloud monitoring tools.

• Understanding of cloud security fundamentals.

• Relevant cloud, Kubernetes or DevOps certifications.

🚀 Our examplary project you may work on

Datumo has delivered an end-to-end ML platform for a global pharmaceutical company operating more than 30 manufacturing facilities. The solution centralized real-time IoT and production data in the cloud, creating high-quality datasets and scalable infrastructure for machine learning. Using Databricks, Azure ML and Vertex AI, the platform supported models recommending optimal PLC configurations to manufacturing engineers. This helped identify quality issues earlier, reduce defective medicine batches and improve production efficiency.

Datumo has also developed a Databricks-based analytics engine for categorizing bank transactions and applying complex business rules to account data. The solution enabled advanced creditworthiness and credit risk assessment based on customers’ transactional behavior. The project was delivered in a highly regulated and secure banking environment, with strict data protection and governance requirements. It shows how Datumo combines scalable data processing and AI to support real financial decisions.

✅ What We Offer

- 20,000 - 30,000 PLN net + VAT / month on B2B,
- 20 days off,
- 100% remote work,
- training budget and support in cloud certifications,
- work with a specialized team of around 60 data experts,
- projects in real enterprise cloud and data environments,
- private medical care, sports package, English lessons and team integrations.

📆 Recruitment Process (Simple & Technical)

1. Soft Skills Interview (30 min): A short discussion about communication and teamwork.
2. Technical Conversation (60 min): A deep-dive discussion with Datumo engineers about MLOps, cloud, Kubernetes and your past projects.
3. Final Decision & Offer.

Apply and tell us briefly what kind of MLOps, cloud, Kubernetes or production platform projects you have worked on.