• Build and maintain monitoring infrastructure for conventional machine learning models, with capabilities for performance tracking, drift detection, and alerting. • Research, evaluate, and implement monitoring strategies and tools for Generative AI systems, including LLMs and Agentic AI architectures. • Collaborate with ML Engineers, Data Scientists, and DevOps teams to deploy, manage, and monitor models in production. • Develop and support scalable, secure, and automated data pipelines using Snowflake, SQL, and Python for training, serving, and monitoring ML and GenAI models. • Leverage AutoML tools and frameworks (e.g., MLflow, Kubeflow, SageMaker Autopilot) to streamline experimentation and deployment. • Design dashboards and reporting systems to visualize model health metrics and surface key operational insights. • Ensure auditability, reproducibility, and compliance for model performance and data flow in production environments, with consideration for regulatory standards like GDPR and HIPAA. • Maintain CI/CD workflows and version-controlled codebases (e.g., Git) for ML infrastructure and pipelines. • Utilize containerization and orchestration technologies (e.g., Docker) to manage scalable ML infrastructure. • Leverage tools such as Streamlit and Python visualization libraries to present insights from model and data monitoring. • Perform root cause analyses on model degradation or data quality issues, and proactively implement improvements. • Stay current on industry developments related to ML observability, model governance, responsible GenAI practices, and AI security. • Contribute to analytics projects and data engineering initiatives as needed. • Provide off-hours support for critical deployments or urgent data/model issues.