Senior MLOps Engineer – Google Cloud

This is a fully remote position, open to applicants in Poland.

📋 Description

• Develop and sustain production-grade ML workflows utilizing Vertex AI and Gemini Enterprise Agent Platform Pipelines.

• Create and enhance reusable components for model training, evaluation, registration, deployment, monitoring, and retraining.

• Execute automated model lifecycle management, incorporating quality controls and approval mechanisms.

• Connect ML pipelines with BigQuery and other Google Cloud services.

• Collaborate with engineering teams to embed ML workflows into CI/CD pipelines and facilitate multi-environment deployment processes.

• Partner closely with Data Scientists to transition machine learning models and experimental code into production.

• Enhance the reliability, observability, scalability, and cost-effectiveness of machine learning workloads.

• Establish monitoring and alerting systems for model performance and platform health.

• Advocate for best practices related to governance, reproducibility, and ML platform standards.

• Participate in technical design discussions and continuous improvement efforts within the MLOps ecosystem.

⛳️ Requirements

• Extensive hands-on experience in MLOps, ML Platform Engineering, or Machine Learning Operations.

• Demonstrated production experience with Vertex AI and/or Gemini Enterprise Agent Platform Pipelines.

• Proficient Python software engineering skills.

• Significant experience with Google Cloud Platform services, particularly BigQuery.

• Experience in constructing modular and reusable ML pipeline components.

• Practical experience with CI/CD practices and tools in production settings.

• Strong comprehension of model versioning, monitoring, retraining strategies, and reproducibility.

• Knowledge of software engineering best practices, testing methodologies, and code quality standards.

• Experience collaborating closely with Data Scientists to convert experimental models into production-ready solutions.

• Proficient in English (C1).

• Google Cloud Professional Machine Learning Engineer certification or its equivalent (preferred).

• Familiarity with infrastructure as code and cloud automation tools (preferred).

• Understanding of cost optimization practices for machine learning workloads (preferred).

• Experience utilizing AI tools in day-to-day workflows (preferred).

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Flexible working hours and remote work opportunities.

• Professional development and training programs.

• Supportive and inclusive work environment.