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
- LLM-powered research tooling for AI Teaching companion combining retrieval systems, experimentation workflows, and backend services
- Geospatial satellite data pipelines surfacing mineral exploration signals from noisy remote sensing data
- Near-real-time energy forecasting pipelines spanning SCADA + weather data ingestion, data warehousing, and ML-driven grid balancing
- Semantic search and recommender system and data products powering infrastructure intelligence and risk assessment for institutional investments
- OCR-driven clinical data processing pipelines transforming unstructured medical documents into usable datasets
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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