Senior MLOps Engineer (Google Cloud)

Spyrosoft is a software engineering company established in 2016. It specializes in technology solutions for industry 4.0, automotive, geospatial, healthcare & life sciences, employee experience & education, and financial services industries. The company was recognized among the fastest growing technology companies in Europe in 2021 and 2022 by the Financial Times.

• Strong hands-on experience in MLOps, ML Platform Engineering, or Machine Learning Operations
• Proven production experience with Vertex AI and/or Gemini Enterprise Agent Platform Pipelines
• Strong Python software engineering skills
• Solid experience with Google Cloud Platform services, especially BigQuery
• Experience building modular and reusable ML pipeline components
• Hands-on experience with CI/CD practices and tools in production environments
• Strong understanding of model versioning, monitoring, retraining strategies, and reproducibility
• Knowledge of software engineering best practices, testing methodologies, and code quality standards
• Experience working closely with Data Scientists and translating experimental models into production-ready solutions
• Fluent English (C1)

Nice to have:

• Google Cloud Professional Machine Learning Engineer certification or equivalent
• Experience with infrastructure as code and cloud automation tools
• Knowledge of cost optimization practices for machine learning workloads
• Experience using AI tools in day-to-day workflow

• Build and maintain production-grade ML workflows using Vertex AI and Gemini Enterprise Agent Platform Pipelines
• Design and develop 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 closely with Data Scientists to productionize machine learning models and experimental code
• Improve reliability, observability, scalability, and cost efficiency of machine learning workloads
• Implement monitoring and alerting mechanisms for model performance and platform health
• Support best practices related to governance, reproducibility, and ML platform standards
• Contribute to technical design discussions and continuous improvement initiatives within the MLOps ecosystem