Tools with purpose.
Systems with substance.
The technologies and practices I bring together across cloud platforms, machine learning workflows, and the data foundations that support them.
Cloud & delivery
Provisioning repeatable environments and building secure paths from commit to production.
- Cloud platformsAzure & AWS
- Cloud nativeKubernetes & Helm
- Infrastructure as codeTerraform, Ansible & Vagrant
- CI/CDGitHub Actions, Azure DevOps & Jenkins
MLOps & serving
Connecting experimentation with reproducibility, orchestration, and operational model serving.
- Experiment trackingMLflow, DVC & reproducibility
- Model servingTriton, KServe & FastAPI
- Workflow orchestrationPrefect & Airflow
Learning systems
Applied machine learning tooling, model architectures, and distributed learning approaches.
- Python ecosystemPyTorch, TensorFlow & scikit-learn
- Applied architecturesConvolutional neural networks & automatic speech recognition
- Distributed learningFederated learning systems
Databases & DataOps
Keeping data infrastructure recoverable, reproducible, and ready for the workloads it supports.
- Oracle administrationRMAN, Data Guard & performance tuning
- DataOpsLiquibase, Flyway & dbt
- Data pipelinesPySpark, Polars & Great Expectations