The work
- Data pipelines and feature stores that turn messy big data into something models can learn from
- Model training, fine-tuning, and evaluation — classical ML and modern LLM / RAG systems
- High-throughput inference and content processing at scale — millions of items, not demos
- MLOps: versioning, monitoring, and retraining so models don’t quietly rot
- Serving on AWS and Google Cloud — SageMaker, Vertex AI, GPU and serverless, cost-aware
Big data and content at scale
We build systems that ingest, enrich, and serve large volumes of content and events. The hard part is rarely the model — it’s the pipeline, the latency budget, and the bill. We design for all three.
Honest about scope
We won’t bolt an LLM onto a problem that doesn’t need one. If a smaller model, a rules layer, or simply better data solves it cheaper, we’ll tell you.