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Experience working on machine learning platforms or feature stores.
Hands-on experience with vector databases (e.g., FAISS, Milvus), graph databases (e.g., Neo4j), or time-series databases (e.g., InfluxDB, Prometheus).
Experience with one or more of the following: BigQuery, HBase, Aerospile, Kafka, Flink, Spark (or Dataproc), or similar systems.
Solid understanding of the AI/ML development lifecycle, with familiarity in MLOps practices such as model deployment, serving, and monitoring.
Knowledge about public cloud infrastructure such as Google cloud platform is a strong plus
Knowledge of data governance, access control, and privacy practices in feature/data platforms.
Contributions to open-source ML infrastructure or platform projects is a plus.
Java + Spring boot or Python
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