Senior GCP Engineer with Vertex AI and MLOps
Remote (Cincinnati, OH)
Contract
Required:
- Senior GCP engineer with atleast 7 years on project experience.
- Worked with Vertex AI and ML/Ops.
- Can work independently with the data science team.
- Overall Communication needs to be good.
Description:
- As a Sr. Data Engineer, you will have the opportunity to lead the development of innovative data solutions, enabling the effective use of data across the organization.
- You will be responsible for designing, building, and maintaining robust data pipelines and platforms to meet business objectives, focusing on data as a strategic asset.
- A strong emphasis will be placed on expertise in GCP, Vertex AI, and advanced feature engineering techniques.
Key Responsibilities:
- Provide Technical Leadership
- Build and Maintain Data Pipelines: Design, build, and maintain scalable, efficient, and reliable data pipelines to support data ingestion, transformation, and integration across diverse sources and destinations, using tools such as Kafka, Databricks, and similar toolsets.
- Drive Digital Innovation: Leverage innovative technologies and approaches to modernize and extend core data assets, including SQL-based, NoSQL-based, cloud-based, and real-time streaming data platforms.
- Implement Feature Engineering: Develop and manage feature engineering pipelines for machine learning workflows, utilizing tools like Vertex AI, BigQuery ML, and custom Python libraries.
- Implement Automated Testing: Design and implement automated unit, integration, and performance testing frameworks to ensure data quality, reliability, and compliance with organizational standards.
- Optimize Data Workflows
- Mentor Team Members
- Draft and Review Documentation: Draft and review architectural diagrams, interface specifications, and other design documents to ensure clear communication of data solutions and technical requirements.
- Cost/Benefit Analysis: Present opportunities with cost/benefit analysis to leadership, guiding sound architectural decisions for scalable and efficient data solutions