Google Cloud Platform Vertex AI Sr. Data Engineer
Posted 1 day ago by 1753945301
Negotiable
Outside
Remote
USA
Summary: The role of Google Cloud Platform Vertex AI Sr. Data Engineer involves leading the integration of machine learning models into critical business applications using Google Cloud Platform Vertex AI. The position requires collaboration with various stakeholders to ensure seamless deployment and performance of models in production environments. Responsibilities include designing scalable inference pipelines, optimizing model performance, and automating lifecycle management. The role is remote and classified as outside IR35.
Key Responsibilities:
- Lead the integration of machine learning models into business-critical applications using Google Cloud Platform Vertex AI.
- Collaborate with data engineers, data scientists, software engineers, and product owners to ensure seamless model deployment and performance in production environments.
- Design and implement scalable, resilient, and secure model inference pipelines using Vertex AI, Vertex Pipelines, and related services.
- Enable continuous delivery and monitoring of models via Vertex AI Model Registry, Prediction Endpoints, and Model Monitoring features.
- Optimize model serving performance, cost, and throughput under high-load, real-time, and batch scenarios.
- Automate model lifecycle management including CI/CD pipelines, retraining, versioning, rollback, and shadow testing.
- Participate in architecture reviews and advocate best practices in ML model orchestration, resource tuning, and observability.
Key Skills:
- Minimum of 2 years - Strong experience in model integration and deployment using Google Cloud Platform Vertex AI.
- Especially around Vertex Pipelines, Endpoints, Model Monitoring, and Feature Store.
- Python (with emphasis on integration frameworks and automation).
- Git, Docker, Poetry, Terraform or Deployment Manager.
- BigQuery, Dataflow, and Cloud Functions.
- Monitoring tools (Stackdriver, Prometheus, etc.).
- Expertise in scaling ML models in production, including load balancing, latency optimization, A/B testing, and automated retraining pipelines.
- Proficiency in MLOps and model operationalization techniques, with knowledge of infrastructure-as-code and containerized environments.
Salary (Rate): undetermined
City: undetermined
Country: USA
Working Arrangements: remote
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: IT
Detailed Description From Employer:
Below are the MUST have Required Skills:
- Minimum of 2 years - Strong experience in model integration and deployment using Google Cloud Platform Vertex AI (Required)
- Especially around Vertex Pipelines, Endpoints, Model Monitoring, and Feature Store
- Python (with emphasis on integration frameworks and automation)
- Git, Docker, Poetry, Terraform or Deployment Manager
- BigQuery, Dataflow, and Cloud Functions
- Monitoring tools (Stackdriver, Prometheus, etc.)
- Expertise in scaling ML models in production, including load balancing, latency optimization,
- A/B testing, and automated retraining pipelines.
- Proficiency in MLOps and model operationalization techniques, with knowledge of infrastructure-as-code and containerized environments.
Preferred Skills
- Experience with MLOps tools such as Kubeflow, MLFlow, or TFX.
- Familiarity with enterprise monitoring tools like Prometheus, Grafana, or Stackdriver for ML observability.
- Exposure to hybrid or federated model deployment architectures.
Day to Day Responsibilities
Lead the integration of machine learning models into business-critical applications using
Google Cloud Platform Vertex AI.
Collaborate with data engineers, data scientists, software engineers, and product owners to
ensure seamless model deployment and performance in production environments.
Design and implement scalable, resilient, and secure model inference pipelines using Vertex
AI, Vertex Pipelines, and related services.
Enable continuous delivery and monitoring of models via Vertex AI Model Registry, Prediction
Endpoints, and Model Monitoring features.
Optimize model serving performance, cost, and throughput under high-load, real-time, and
batch scenarios.
Automate model lifecycle management including CI/CD pipelines, retraining, versioning,
rollback, and shadow testing.
Participate in architecture reviews and advocate best practices in ML model orchestration,
resource tuning, and observability.