Senior MLOps Engineer (Google Cloud)
S
SpyrosoftMachine learning
Workplace type: remote; Locations: Warszawa, Prosta 20, Wrocław, plac Nowy Targ 28, Kraków, Zabłocie 43, Łódź, Al. Piłsudskiego 24, Białystok, Świętojańska 12A, Warszawa, Country code: PLContractSenior
Salary140 - 170 PLN per hour net b2b
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Job Details
- Languages
- Strong Polish communication skills (minimum B2), both written and verbal; Fluent English (C1)
- Required Skills
- PythonGCPCI/CDBigQueryMLOps
Requirements
- Have strong hands-on experience in MLOps, ML Platform Engineering, or Machine Learning Operations.
- Have production experience with Vertex AI and/or Gemini Enterprise Agent Platform Pipelines.
- Bring strong Python software engineering skills.
- Have experience with Google Cloud Platform services, especially BigQuery.
- Have experience building modular and reusable ML pipeline components.
- Have hands-on experience with CI/CD practices and tools in production environments.
- Understand model versioning, monitoring, retraining strategies, and reproducibility.
- Know software engineering best practices, testing methodologies, and code quality standards.
- Have experience working with Data Scientists to translate experimental models into production-ready solutions.
- Have strong Polish communication skills, minimum B2, written and verbal.
- Be fluent in English (C1).
- Google Cloud Professional Machine Learning Engineer certification or equivalent is nice to have.
- Infrastructure as code and cloud automation experience is nice to have.
- Knowledge of cost optimization for machine learning workloads is nice to have.
Responsibilities
- Build and maintain production-grade ML workflows using Vertex AI and Gemini Enterprise Agent Platform Pipelines.
- Design reusable components for model training, evaluation, registration, deployment, monitoring, and retraining.
- Implement automated model lifecycle management, including quality controls and approval processes.
- Integrate ML pipelines with BigQuery and other Google Cloud services.
- Collaborate with engineering teams to integrate ML workflows into CI/CD pipelines and multi-environment deployment processes.
- Work with Data Scientists to productionize machine learning models and experimental code.
- Improve the reliability, observability, scalability, and cost efficiency of machine learning workloads.
- Implement monitoring and alerting for model performance and platform health.
- Support governance, reproducibility, and ML platform standards.
- Contribute to technical design discussions and continuous improvement initiatives within the MLOps ecosystem.
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