Design and build a reliable AI/ML platform that moves models from experimentation to production across cloud and devices. Develop model training, deployment, monitoring, and governance capabilities while partnering with data science, engineering, security, quality, and regulatory teams.
Responsibilities
- Define architecture and roadmap for AI platform components, including feature stores, training pipelines, registries, deployment, and serving.
- Connect curated data and ML features while maintaining data lineage from sensor readings to model outputs.
- Create templates, tools, and documentation for self-service model training and deployment.
- Extend CI/CD pipelines with model testing, validation gates, and approval workflows.
- Implement model, data drift, and sensor-quality monitoring, alerting, and rollback.
- Build device model delivery pipelines and support LLM service deployment, evaluation, and versioning.
Required Skills
- Python
- Azure ML SDKAzure Data Factory
- Databricks
- MLflow
- Kubeflow
- Machine learning platform architecture
- ML training and inference pipelines
- CI/CD for machine learning
- Model monitoring and observability
