- Design, build, and operate MLOps pipelines supporting the full ML lifecycle (training, validation, deployment, monitoring).
- Enable production workloads for AI/ML and Generative AI systems, including LLM-based services.
- Develop and maintain CI/CD pipelines for AI/ML services and supporting infrastructure.
- Build and manage cloud-native infrastructure on AWS, with heavy use of Kubernetes and containerized workloads.
- Automate infrastructure provisioning and configuration using Infrastructure as Code (Terraform).
- Implement model versioning, experiment tracking, and artifact management across environments.
- Ensure reliability, scalability, observability, and cost efficiency of AI platforms.
- Partner with AI/ML engineers to operationalize models and standardize deployment patterns.
- Implement monitoring and alerting for system health, model performance, and drift.
- Enforce security, compliance, and governance requirements for AI workloads.
- Participate in incident response, root cause analysis, and continuous improvement initiatives.
- Document standards, best practices, and reference architectures for MLOps and AI infrastructure.
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