Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Docker @ 3
Kubernetes @ 3
DevOps @ 3
Spark @ 3
GCP @ 3
ETL @ 3
Airflow @ 3
CI/CD @ 3
Machine Learning @ 3
MLOps @ 3
Vertex AI @ 3
Hiring @ 3
MLFlow @ 3
AI @ 3
Data Pipelines @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
ING Hubs Poland is hiring for an MLOps Engineer to join the ML-Batch squad within the Data Analytics Platform (DAP).
Expected salary: 9600 - 19000 PLN
Role overview
You will work on developing and operating a robust, scalable platform for batch processing, ETL and machine learning pipelines. The team focuses on leveraging Airflow and MLflow and applying MLOps practices to enable reproducible, scalable and automated ML workflows. The global job architecture naming for this role is "Engineer III."
Responsibilities
- Develop and manage workflow orchestration using Apache Airflow (core responsibility).
- Support and improve model lifecycle management using MLflow (tracking, registry, reproducibility).
- Contribute to migration of the ML Batch platform from on-premise (IPC) to Google Cloud Platform (GCP).
- Refactor and adapt ML pipelines to run efficiently in cloud-native environments.
- Develop and improve templates for productionizing ML solutions.
- Integrate ML pipelines with CI/CD pipelines for automated deployments.
- Ensure scalability, reliability and reproducibility of ML workloads.
- Troubleshoot and optimize pipelines to improve performance and stability.
- Participate in on-call support to maintain platform reliability.
- Collaborate with stakeholders (data scientists, engineers) to deliver scalable, secure and cost-efficient solutions.
Requirements
- Good understanding of machine learning model deployment and consumption patterns.
- Hands-on experience with workflow orchestration tools, especially Apache Airflow (must-have).
- Experience with ML lifecycle management tools such as MLflow (strongly preferred).
- Hands-on experience with Google Cloud Platform (GCP) in the context of data or ML pipelines (e.g., BigQuery, Vertex AI, Cloud Storage or similar).
- Experience building containerized components (Docker).
- Experience in CI/CD and DevOps practices.
- Hands-on experience with data pipelines and ETL processes.
- Hands-on experience with monitoring, logging and troubleshooting ML pipelines.
- Ability to express ideas clearly and collaborate effectively with data scientists and engineers.
- English language proficiency at B2 level or above.
Nice to have / Extra points
- Strong experience with Airflow-based workflow design and optimization.
- Experience with Vertex AI in GCP.
- Experience with Spark or distributed batch data processing.
- Familiarity with Kedro or similar pipeline frameworks.
- Experience with Kubernetes or distributed environments.
Information about the squad
ML-Batch provides an easy-to-use platform for designing and implementing batch processing, ETL and machine learning pipelines. The team emphasizes MLOps practices (CI/CD, deployment, monitoring) to enable reliable, high-performance data workflows for data scientists across ING.