Overview
Remote
$140,000 - $150,000
Full Time
Skills
GCP
Google Cloud Platform
MLOps
Machine Learning
Kubeflow
AWS
Azure
Kubernetes
Python
SQL
Devops
Docker
Job Details
Google Cloud Platform Data ML Ops Engineer
Job Description:
A Google Cloud Platform Data ML Ops Engineer is responsible for designing, deploying, and managing machine learning pipelines and infrastructure on Google Cloud Platform (Google Cloud Platform). They bridge the gap between data scientists and operations teams, ensuring models are production-ready, reliable, and continuously improving.
Key Responsibilities:
- Machine Learning Deployment and Integration: Operationalizing and deploying machine learning models, designing APIs for model communication, and implementing strategies for model monitoring, versioning, and updates.
- Data Pipeline Architecture and Management: Designing, implementing, and managing end-to-end data pipelines for collecting, processing, and storing data; ensuring data quality, consistency, and availability.
- Monitoring and Optimization: Implementing monitoring solutions to track system and model performance, analyzing metrics for proactive optimization, and improving system efficiency.
- Automation and CI/CD: Automating data pre-processing, model training, and deployment workflows; optimizing CI/CD pipelines for machine learning models.
- Collaboration and Documentation: Collaborating with data scientists, software engineers, and robotics teams, and maintaining documentation for deployed models, data pipelines, and system architecture.
- Infrastructure Management: Designing and managing scalable infrastructure on Google Cloud Platform for training, testing, and serving machine learning models.
- Security and Compliance: Ensuring ML systems meet security and compliance standards, including data protection and privacy regulations.
Qualifications:
Required:
- Education: A Bachelor's degree or higher in a relevant field such as Computer Science or Data Science is typically required.
- Experience: Previous experience in MLOps, DevOps, or related areas, with a focus on Google Cloud Platform and its services, is essential.
- Technical Skills: Key technical skills include proficiency in programming languages like Python and SQL, experience with MLOps platforms (e.g., MLflow, Kubeflow), understanding of containerization (Docker) and orchestration (Kubernetes), knowledge of cloud platforms (AWS, Azure, Google Cloud Platform), and familiarity with data engineering concepts.
- Soft Skills: Strong problem-solving, communication, and teamwork abilities are necessary.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.