Project description:

Join a team focused on building and scaling enterprise-grade machine learning platforms on Google Cloud. As a Senior MLOps Engineer, you will play a key role in transforming machine learning architectures into reliable, production-ready solutions. Working closely with ML Architects, Data Scientists, and Cloud Engineers, you will design and develop reusable platform capabilities that support the entire ML lifecycle, from model training and validation to deployment, monitoring, and automated retraining.

You will contribute to creating robust MLOps standards, improving operational excellence, and enabling teams to deliver machine learning solutions faster, safer, and more efficiently across enterprise environments.

Tech stack:

• Google Cloud Platform (GCP)

• Vertex AI

• Gemini Enterprise Agent Platform Pipelines

• BigQuery

• Python

• CI/CD

• Docker

• ML Monitoring & Observability

• Model Registry & Versioning

• Git

Requirements:

• 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

Main responsibilities:

• 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