I design pipelines and models that stay correct under late data, scale, and real-world failure.
Currently working on audit analytics, agentic backends, and production forecasting pipelines.
- Designing ingestion and modeling systems for messy, high-volume event data - Production ML and LLM workflows with evaluation, monitoring, and deployment hygiene - Resilient integrations handling rate limits, backfills, schema drift, and retries
- Building a Google Workspace audit analytics pipeline with overlap-safe ingestion - Developing agentic backend workflows using LLMs - Writing about real-world data failures and system design tradeoffs
Pinned repositories below reflect the work above.
Open to Data Engineering, MLOps, and Platform roles. Best reached via LinkedIn or email.
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