Location: Argentina (UTC-3)

Remote: Yes

Willing to relocate: No

Technologies: ML infrastructure, distributed systems, real-time/streaming data, online inference, on-device/edge ML; Scala, Python, SQL; Kafka, Flink, Spark, Pinot; Kubernetes; TensorFlow/TFLite, PyTorch; MLOps; AWS/GCP

Résumé/CV: https://ianreppel.org/cv

Email: hn@ianreppel.org

Technical product manager for ML and data infrastructure (B2C, B2B, and deep tech) with a PhD in theoretical physics. I own problem discovery, architecture trade-offs, and end-to-end delivery with measured outcomes ($110M+ impact).

~15 years building data/ML infrastructure (cloud and air-gapped on-prem). Built and operated ML platforms and streaming systems in environments exceeding ~$5B+ in annual transaction volume.

Former engineer and still hands-on; active open-source contributor (Zoose Quantum, IBM Qiskit ecosystem). Owned delivery of a large physics-based foundation model trained over multi-month HPC runs from lab to production. Trained and deployed large-scale production models; built on-device (edge) inference prototypes. Earlier work includes database/query optimization: oracle.rtfd.io (~100 pages).