Sr. Data Engineer,Location : 100./. REMOTE ,Duration: 6+ Months contract

Summary: The Sr. Data Engineer role involves leading the integration of machine learning models into critical applications using Google Cloud Platform Vertex AI. The position requires collaboration with various teams to ensure effective model deployment and performance monitoring. The role emphasizes designing scalable inference pipelines and automating model lifecycle management. Candidates should have extensive experience in model integration and deployment within cloud environments.

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:

• 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.
• 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.
• Google Cloud Platform Vertex AI Suite (including Pipelines, Feature Store, Model Monitoring).
• 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.).
• Experience with MLOps tools such as Kubeflow, MLFlow, or TFX.
• Familiarity with enterprise monitoring tools Prometheus, Grafana, or Stackdriver for ML observability.
• Exposure to hybrid or federated model deployment architectures.

Salary (Rate): undetermined

City: undetermined

Country: USA

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT