Description

Key Responsibilities

• Develop and maintain ML pipelines using tools such as MLflow, Kubeflow, or Vertex AI
• Automate model training, testing, deployment, and monitoring across cloud environments (GCP, AWS, Azure)
• Implement CI/CD workflows for model lifecycle management including versioning, monitoring, and retraining
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
• Provision containerized environments and support model scoring via low-latency APIs
• Leverage AutoML tools (Vertex AI AutoML, H2O Driverless AI) for rapid, low-code model development and deployment
• Implement telemetry, traces, dashboards, SLOs, evaluation suites, and readiness evidence for priority agent releases

Required Skills

• Python, Java, SQL
• ML libraries: scikit-learn, XGBoost, TensorFlow, PyTorch
• MLflow, Kubeflow, Vertex AI
• Cloud platforms: GCP, AWS, Azure
• CI/CD, containerization, low-latency API integration
• LLM and agent evaluation, tracing, telemetry, metrics, and dashboards
• SLOs, test automation, prompt and model performance analysis

Preferred Skills

• Experience with AutoML platforms (Vertex AI AutoML, H2O Driverless AI)
• Background in model governance and MRM frameworks
• Hands-on production operations experience for AI/ML systems

Key Skills

Education

Bachelor's degree